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An evaluation of BASIC computer language as a prerequisite to university computer science Ellis, David Norman 1989

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A N E V A L U A T I O N OF BASIC COMPUTER L A N G U A G E AS A PREREQUISITE TO UNIVERSITY COMPUTER SCIENCE by David Norman Ellis B. Sc., The University of British Columbia, 1968  A THESIS SUBMITTED IN P A R T I A L F U L F I L L M E N T OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS  in  THE F A C U L T Y OF G R A D U A T E STUDIES M A T H E M A T I C S & SCIENCE EDUCATION  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH C O L U M B I A September, 1989 ©  David Norman Ellis, 1989  In  presenting  degree freely  this  at the  thesis  in  partial  fulfilment  University  of  British  Columbia,  available for  copying  of  department publication  this or of  reference  thesis by  this  for  his thesis  and study. scholarly  or  her  for  of  requirements  I agree  I further  purposes  representatives.  financial gain  the  that  agree  may  be  It  is  shall not  that  MATH 1L  The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  HS^  OCTT. II  Library  by  understood be  allowed  epur-/fry ; cA  an  advanced  shall make  permission for  granted  permission.  Department of  the  for  the that  without  it  extensive  head  of  my  copying  or  my  written  ABSTRACT This study was undertaken to determine how prior knowledge of BASIC computer language affects the achievement in introductory computer science courses at university. It looked at comparisons of achievement in introductory computer science courses at the University of British Columbia (U.B.C.) among groups who have learned BASIC, or who have learned other languages, or who have learned no languages. It investigated comparisons of achievement among demographic factors: gender, age, Faculty, and major. Achievement differences in first year FORTRAN courses, in first year Pascal courses and in a second year Pascal course, among groups of students with different backgrounds were also examined. The study investigated the effect on achievement of "how well", "when", and "where" either BASIC or Pascal had been learned. Finally, it identified factors that are the best predictors of success in introductory computer science. A questionnaire was distributed in six introductory computer courses at U.B.C. during the school year 1985-1986. Marks were collected in these courses at the end of the year. After matching by student number a sample of 1194 students was analyzed using the analysis of covariance and the multiple linear regression routines of the Statistical Package for the Social Sciences (SPSS). It was found that students who have taken BASIC language do better in university introductory level computer science courses than those who have no prior knowledge of a computer language, and they do as well as those who have prior knowledge of some other computer language. It was discovered that students who have previously learned a computer language have better achievement than those who have not learned a language. Students with a knowledge of at least two languages have an even higher achievement. The order of learning BASIC language was observed not to be significant in subsequent computer science achievement. Males had higher achievement than females in the surveyed ii  courses. The younger students tended to have higher achievement than the older students in these courses. Achievement differences were found among the Faculties involved. Students who were majoring in mathematics outperformed those who were non-mathematics majors within the Faculty of Arts. Achievement in the second year course did not appear to be dependent upon the computer language background prior to entering university. Students who were able to write complex BASIC programs outperformed others with a limited familiarity with BASIC language in introductory computer science courses. For students who had prior knowledge of Pascal language the level of how well the language had been learned did not appear to be a factor in introductory computer science achievement. The age at which BASIC or Pascal language was first learned was a critical factor. Those who first learned the language in the 13 to 18 age range outperformed others who first learned the language at an older age. The place where either BASIC or Pascal language had been learned did not appear to be a critical factor for achievement in introductory computer science. Only the overall year percent in all other courses taken and the variable as to "how well" BASIC language had been learned, proved to be significant factors in predicting success in introductory computer science. The writer concludes that the results of this study provide sufficient evidence to support the argument that BASIC language should continue being taught in pre-university computer science courses.  i i i  CONTENTS Page LIST OF TABLES  vii  LIST OF FIGURES  ix  ACKNOWLEDGEMENT  x  Chapter 1. INTRODUCTION  1  Purpose of the Study  3  Overview  4  Organization of the Following Chapters  6  2. REVIEW OF THE LITERATURE  7  Predicting Computer Science Achievement  7  Predicting Achievement in General  27  Introductory Computer Science Curriculum  28  Introductory Computer Programming Language  32  3. METHODOLOGY  40  Sample Selection  40  Course Descriptions  41  Questionnaire  43  Pilot Testing  46  Course Grades  47  Questions  47  BASIC Language Backgrounds  47  Backgrounds (non-computer language-related)  48  Different Language Backgrounds  48  iv  Chapter  Page BASIC and Pascal B ackgrounds  49  Achievement Predictors  49  Methods of Analysis  49  4. RESULTS  52  Descriptive Analyses  52  Achievement in Introductory Computer Science Courses Inferential Analyses  66 67  Results on BASIC Language Backgrounds  68  BASIC versus No Language  68  BASIC versus Other Languages  69  BASIC Only versus BASIC and Other  70  BASIC First versus Not BASIC First  71  Number of Computer Languages (0-4)  72  Results on Demographic Backgrounds  73  Gender  73  Age  74  Faculty  75  Major  76  Results on Different Language Backgrounds  78  Different Backgrounds in First Year  78  Different Backgrounds in Second Year  82  Results of BASIC and Pascal Backgrounds  83  "How Well" Language was Learned  83  "When" Language was Learned  86  "Where" Language was Learned  88  v  Chapter  Page Results of Achievement Predictors  90  Achievement Predictors  90  5. DISCUSSION  92  Summary and Conclusions  92  BASIC Language Backgrounds  92  Demographic Backgrounds  95  Different Language Backgrounds  97  BASIC and Pascal Backgrounds  98  Achievement Predictors  100  Implications  100  Limitations  101  Suggestions for Further Research  102  BIBLIOGRAPHY  104  APPENDIX A.  Letter to Instructors  117  B.  Coding Scheme  119  C.  Statistical Tests  123  D.  Personal Communications  141  vi  LIST OF TABLES Table  Page  1  Number of Sections Surveyed  41  2  Number of Responses to the Questionnaire  41  3  Distribution of Students in Computer Courses  53  4  Distribution of Students by Year of Registration  54  5  Distribution of Students by Faculty  54  6  Order of Learning BASIC for Students with Prior Knowledge of BASIC Language  56  7  Order of Learning Pascal for Students with Prior Knowledge of Pascal Language  58  8  Distribution of Students by Number of Computer Science Courses Taken  62  9  Comparison of Marks in Computer Science Course with Averages in all Other Courses Comparison of Results of Students Who Learned BASIC as the First Language and Students Who Knew No Languages  67  10 11 12 13  14  69  Comparison of Results of Students Who Had Learned BASIC and Students Who Had Learned Another Language  70  Comparison of Results of Students Who Had Learned Only BASIC and Students Who Had Learned BASIC and Another Language  71  Comparison of Results of Students Who Had Learned BASIC First and Students Who Had Learned BASIC Other than as a First Language  72  Comparison of Results of Students with Various Number of Computer Languages Learned  73  15  Comparison of Students Results by Gender  74  16  Comparison of Students Results by Age  75  17  Comparison of Students Results by Faculty  76  18  Comparison of Students Results for Selected Majors  77  19  Number of Non-mathematics Majors in the Faculty of Arts  78  vii  Table 20 21 22 23 24 25 26 27 28  Page Comparison of Results Based on Computer Language Background in the FORTRAN (CPSC 101 and CPSC 151) Courses  80  Comparison of Results Based on Computer Language Background in the Pascal Language (CPSC 114 and CPSC 118) Courses  81  Comparison of Results Based on Computer Language Background in the Second Year (CPSC 210) Course  83  Comparison of Results Based on "How Well" BASIC Language had been Previously Learned  85  Comparison of Results Based on "How Well" Pascal Language had been Previously Learned  86  Comparison of Results Based on "When" BASIC Language had been Previously Learned  87  Comparison of Results Based on "When" Pascal Language had been Previously Learned  88  Comparison of Results Based on "Where" BASIC Language had been Previously Learned  89  Comparison of Results Based on "Where" Pascal Language had been Previously Learned  90  viii  LIST OF FIGURES Figure  Page  1  Courses Involved  42  2  The Questionnaire  44  3  Distribution of Students with Prior Knowledge of a Computer Language  55  4  Distribution of Students with Prior Knowledge of BASIC Computer Language  56  5  Distribution of Students with Prior Knowledge of Pascal Computer Language  57  6  First Learned Computer Language  59  7  BASIC, Pascal, LOGO Languages Learned  60  8  Languages First Learned by 835 Students with Prior Knowledge of a Computer Language Order of Learning each Computer Language for Students with Prior Knowledge  61 62  Distribution of Students with Prior Knowledge of a Computer Language by Course  63  Distribution of Students with Prior Knowledge of BASIC Language by Course  64  Distribution of Students with Prior Knowledge of Pascal Language by Course  64  Distribution of Students with Prior Knowledge of LOGO Language by Course  65  Distribution of Students with Prior Knowledge of Other Languages by Course  65  Percentages by Gender with Prior Knowledge of Computer Languages  66  9 10 11 12 13 14 15  ix  ACKNOWLEDGEMENT I would like to thank the members of my thesis committee, Dr. Marv Westrom, Dr. James Sherrill, and Dr. Hugh Dempster for their guidance. It has been a privilege for me to work with them. I am endebted to the efforts of the members of the Eric Hamber School Data Processing Service Club for doing much of the typing of this document. Thanks also, to their sponsor, Mr. Tim Ireland. I would like to express my gratitude to the U.B.C. computer science students who agreed to participate in the study and to their instructors for granting me permission to interrupt their classes. Finally, I would like to thank my family for their encouragement during the study, especially my wife Brenna for her patience and constant support.  x  Page-1  Chapter One  Introduction  This is an information age, with computer technology being responsible, in part, for recent increases in world productivity. Thus, as Andrew Molnar (1978) said "...computer literacy is a prerequisite to effective participation in an information society and as much a social obligation as reading literacy" (p.37). In order for students to participate effectively in the information age one must introduce computer science curricula into schools and post-secondary institutions. The most common computing course, or the largest single topic within a computing course in the school system, at present, and over the past twenty years, is "computer programming". In the school system this "computer programming" course is taught using BASIC language in a majority of situations (Woodhouse, 1983). Charles Schulz (1984) claimed that one of the most important computer-related backgrounds desired for entry into college or university programs in the U.S.A. is a background in BASIC, Pascal or FORTRAN. Currently, there is considerable debate about the merits of teaching BASIC as the introductory computer programming language. (Hereafter, the writer will use the term "language" to refer to "computer programming language".) It is acknowledged that BASIC is widely used and comparatively easy to learn. Malkit Sail (1986) wrote "BASIC is the only language that comes free of cost and is resident in almost all personal computer systems" (p.49). It was developed in 1965 by John G. Kemeny and Thomas E. Kurtz of Dartmouth College primarily as a language for introductory computer science courses. However, as noted below, some computer educators claim that it is a mistake to teach BASIC as a first language. Alfred Bork (1982b) stated BASIC is the junk food of modern programming. ... It is becoming clear that students who learn BASIC as their first computer language will in almost all cases acquire a set of bad programming habits. These habits are very difficult to overcome, so BASIC programmers have difficulty writing readable and maintainable code (p. 12).  Introduction  Page 2  Another distinguished computer scientist, Edsger Dijkstra (1982) claimed that "It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration" (p. 14). Roy Atherton (1982a) (author of a text on COMAL programming language (1982b)) wrote an article entitled "BASIC damages the Brain" in which he explained how a student of his was insistent on programming in BASIC and was reluctant to use more modem techniques. He added "Her brain was damaged and it proved difficult, though not impossible, to effect a cure" (p.16). Borge Christensen (an author of COMAL - a programming language which combines "...the simplicity of BASIC with the power of Pascal" (1982b, p.6)) stated that "BASIC was seriously and fundamentally defective" (1982a, p. 18). A common complaint of BASIC language is that it is not structured. Structured programs have fewer errors, are readable and easy to modify (Bork, 1981). Kernighan and Plauger (1974) claim that "...even the techniques of structured programming do not ensure that code will be good; 'structured' programs can be just as bad as their unstructured counterparts" (p.303). They indicate that in any programming language "...the presence of bad features is not an invitation to use them, nor is the absence of good features an excuse to avoid simulating them as cleanly as possible. Good programming languages are nice, but not vital" (p.319). Shapiro (1980) obtained results that suggest that too great an emphasis on structured programming leads to poor programming style and incorrect implementation of straightforward algorithms. Ralston (1971) states that "...whatever training the student in a first course receives in good programming technique should be almost independent of the language used for implementation.... choice of language is very much a second order effect compared to quality of instruction" (p.28). He adds that if a "perfect" language were used in a first year course, students might be misled into thinking that all is beauty and elegance in computer science, and not see its imperfections. Ralston claims that a beginning computer student may be unable to grasp significant  Introduction  Page 3  concepts in depth. Thus, is too much emphasis being placed on the programming language employed and not enough on good teaching, or other aspects of the language? Whether BASIC should or should not be taught, it is slowly being replaced by other computer languages in many educational institutions. Agee (1985) was asked if "...we should downgrade the use of BASIC in favour of another language". He concluded that because BASIC is so widely used in the industrial/business community and is provided with nearly every available microcomputer that "...students need to learn BASIC for the real world of the 1980's and probably into the 21st century" (p. 13). He pointed out that it is important that students learn what they need and not what educators want them to learn. Tesler (1984) concurs that BASIC "...has fallen from favor somewhat in the academic world" (p.72). But he finds it impossible to select a best programming language because of the great diversity of languages. He concludes that a language must be chosen for the purpose intended. Purpose  o ft h e S t u d y  The purpose of this study is to determine how the prior knowledge of BASIC language affects the achievement in introductory computer science courses at university. To this end the study compares the achievement in introductory computer science courses of a group with a prior knowledge of BASIC language with that of other groups who have learned other languages, or have no prior knowledge of any language. It also compares achievement among groups with different backgrounds, such as gender, age, Faculty and major. The study assesses the differences in achievement in first year FORTRAN and Pascal courses and in a second year computer science course among groups with different computer language backgrounds. It ascertains the impact on achievement of "how well", "when" and "where" a previous language was learned. Finally, the study identifies the factors that are the best predictors of success in introductory courses of computer science.  Introduction  Page 4  The implications this study holds for the computer curriculum in secondary schools is its raison d'etre. If those students who first learned B A S I C have lower achievement than the group who did not learn B A S I C first, then the school system using B A S I C as the first language may need to reconsider its use. A t the same time, the study's results may be of interest to computer software developers for future undertakings. This study may provide valuable information to universities who are presently admitting into computer science programs a large number of students, with widely diversified backgrounds. The universities might use the information for counselling purposes, for course designing, or for limiting enrolments. If those students who first learned B A S I C do not have lower achievement than other groups, the school system does not need to change from instruction in B A S I C language to one that is structured, or free from the "bad" features of B A S I C , without more extensive studies. This could potentially save educational systems and school districts a great deal of money in software, hardware and i n the costs of retraining teachers.  Overview Questions this study w i l l attempt to answer include: 1  How does introductory computer science achievement compare between the group of students whose first computer language was B A S I C and the group who knew no languages?  2  Does achievement differ between the group with prior knowledge of B A S I C and the group who had prior knowledge of computer languages other than B A S I C ?  3  Does achievement differ between the group with prior knowledge of B A S I C only and the group who had prior knowledge of B A S I C and another language?  4  O f all students with prior knowledge of B A S I C does achievement differ between the group who had learned B A S I C first and the group who learned another language before learning B A S I C ?  Introduction  5  Page 5  Are there differences in achievement among the groups of students who have already learned 0, 1,2, 3, or 4 computer languages?  6 Is there a difference in achievement by gender? 7  Are there differences in achievement among various age groups?  8 Are there differences in achievement among the Faculties represented? 9  Is there a difference in achievement between students who are majoring in mathematics and students who are majoring in various fields within the Faculty of Arts (i.e. non-mathematics majors)?  10  Is there a difference in achievement among students with different computer language backgrounds enrolled in the first year FORTRAN (CPSC 101 or CPSC 151) or Pascal (CPSC 114 or CPSC 118) courses?  11  Is there a difference in achievement among students with different computer language backgrounds enrolled in the second year course (CPSC 210)?  12  Are there differences in achievement among the groups that respond in five different ways regarding "how well" a previous language (either BASIC or Pascal) was learned?  13  Are there differences in achievement among the groups that respond in three different ways regarding "when" either BASIC or Pascal was learned?  14  Are there differences in achievement among the groups that respond in four different ways regarding "where" either BASIC or Pascal was learned?  15  Which factor(s) under investigation is/are the best predictor(s) of success in university introductory courses in computer science?  Organization of the Following Chapters A review of the literature pertaining to the experimental questions, a description of the methodology, the results of statistical analysis and a discussion of the findings of the study are found in the following chapters. The review of the literature is presented in Chapter Two. Chapter Three contains the details of the sample selection, the courses involved, the development and administration of the questionnaire, the pilot testing, other data used, the statement of the questions, and the methods of data analysis. The research hypotheses and the results of the descriptive and inferential analyses are discussed in Chapter Four. The conclusions are formulated in Chapter Five together with a discussion of the findings and their implications. This final chapter also includes limitations of the study and areas for further research.  Page~7  Chapter Two  Review of the Literature  Few studies have looked at the effects of secondary school computer science programs on the ensuing achievement in introductory computer science courses at university. Do students with secondary school experience in computer science perform at different levels, i.e. either higher or lower levels of achievement in university computing courses than students that have taken no formal secondary school computing courses?  Predicting Computer Science Achievement Over the last twenty years, many studies have been conducted to predict academic achievement in an introductory computer science course at the secondary school, college or university level (Alspaugh, 1970 and 1972; Barker and Unger, 1983; Bateman, 1973; Bauer, Mehrens and Vinsonhaler, 1968; Buff, 1972; Butcher and Muth, 1985; Cafolla, 1987; Campbell and McCabe, 1984; Capstick, Gordon and Salvadori, 1975; Cheney, 1980; Correnti, 1969; Dey and Mand, 1986; Dixon, 1987; Fowler and Glorfeld, 1981; Gathers, 1986; Glorfeld and Fowler, 1982; Gray, 1974; Greer, 1986; Guinan and Stephens, 1988; Hostetler, 1983; Howerton, 1988; Hunt and Randhawa, 1973; Konvalina, Wileman and Stephens, 1983; Kurtz, 1980; Leeper and Silver, 1982; Lemos, 1981; McGee, Polychronopoulos and Wilson, 1987; Mazlack, 1976; Mazlack, 1980; Mussio and Wahlstrom, 1971; Newsted, 1975; Nowaczyk, 1983; Oman, 1986; Petersen, 1976; Petersen and Howe, 1979; Plog,1980; Ramberg and Van Caster, 1986; Sauter, 1986; Schroeder, 1978; Sharma, 1987; Sorge and Wark, 1984; Stephens, Wileman and Konvalina, 1981; Stephens, Wileman, Konvalina andTeodoro, 1985; Stevens, 1983; Szymczuk and Frerichs, 1985; Tillman, 1974; Werth, 1986; Whipkey and Stephens, 1984; Wileman, Konvalina and Stephens, 1981; Wileman, Stephens and Konvalina, 1982; Yaney, 1970). One purpose of this chapter is to consider the findings of these studies.  Review of the Literature  Page 8  Many different factors have been used as predictor variables of the success in introductory post-secondary computer courses. There is, however, an apparent absence of articles in which university computer science course achievement has been predicted using secondary school computer science performance as one of the predictors. Bauer, Mehrens and Vinsonhaler (1968) performed some preliminary research for the development of a test battery for the selection of computer programmers. They examined the validity of the IBM Aptitude Test for Programmer Personnel (ATPP) and the Strong Vocational Interest Blank (SVIB). Two other predictor variables used were the College Qualification Test (CQT) and grade point averages (GPA's). The best single predictor was GPA (r=0.68). They concluded that numerical reasoning (CQT Numerical) and spatial reasoning (ATPP Part U-figure series) appear to be two of the most important cognitive abilities needed for success as a computer programmer. Correnti (1969) investigated characteristics of students enrolled in computer programming courses to determine whether any of them could be useful for predicting success in programming courses. His study used as independent variables programming aptitude, study habits and attitudes, scholastic aptitude, occupational level of the parents, and academic achievement as measured by students' previous GPA and their college entrance examination results. He concluded that these variables are significant indicators of programming success. The "above-average" achievers were significantly different from the "belowaverage" ones on the variables studied. Yaney (1970) also did a study to predict programming performance. He developed a paper and pencil test that was administered both before and after a training program. He found that programming performance could be predicted accurately at both times, with the posttest yielding a high(er) degree of confidence.  Review of the Literature  Alspaugh (1970) investigated the relationships between selected student characteristics and proficiency in two different programming languages (Basic Assembly Language and FORTRAN). She found that the mathematics background of computer programming students appeared to be the major influencing component for achievement in programming. The more successful programming student possesses a low level of "impulsiveness" and "sociability" and a high level of "reflectiveness". Since Alspaugh had similar findings in the two computer languages, she concluded that computer programming aptitude probably doesn't vary from computer language to computer language. Since most of the variance in programming proficiency was not explained she stated that there are obviously other predictors yet to be identified. Alspaugh (1972) concluded that the placement of computer programming classes within the mathematics curriculum is justified. However, "it would not be realistic to assume that all students who are talented in mathematics will also be talented in computer programming" (p.98). Mussio and Wahlstrom (1971) analyzed the predictive validity of a number of tests in order to determine which one(s) predict(s) most accurately success in a course for computer programmers. From the Computer Programmer Aptitude Battery (CPAB), the Thurstone Test of Primary Mental Abilities (PMA), the SVIB, and a revised form of Russell's Scale of Motivation for School Achievement, the best single predictor of academic performance was the diagramming test of the CPAB, correlating 0.49 with the course grade. The best combination of predictors, using stepwise regression procedures, was the chagramming test, the programmer key and age-related interest scale of the SVIB, and Russell's test of motivation, correlating 0.67 with the criterion measure, the authors conclude that "reasoning ability is the single most important qualification for programmers" (p. 34) in addition to certain personality measures. Buff (1972) used ten independent variables to provide measurements of aptitude, achievement, and socioeconomic status in order to develop a method for prediction of academic  Review of the Literature  Page 10  achievement in FORTRAN programming courses. He found that achievement scores correlated more highly with the dependent variable than did the aptitude scores, almost without exception. The GPA proved to be the best single predictor. Of the socioeconomic variables, Buff found that the father's occupation and the father's education correlated negatively with the dependent variable. The hypotheses that student achievement can be predicted from aptitudes, abilities, prior academic performance, and other socioeconomic characteristics, were upheld. Hunt and Randhawa (1973) attempted to ascertain the relationship between 12 cognitive variables, and the final mark in a university introductory computer science course. They used raw scores from seven tests taken from the Kit of Reference Tests for Cognitive Factors and five subtests of the Watson-Glaser Test of Critical Thinking (CT) as the predictor variables. They found using a stepwise regression analysis that the Locations, Hidden Patterns, and Seeing Problems from the reference tests, and Deduction from the CT test accounted for only 23.1% of the variance of the criterion variable. Bateman (1973) used 16 independent variables in order to predict a course grade in an introductory computer course, for the purpose of counselling and/or screening students. Among the independent variables that he used were prior programming experience, an I.B.M. aptitude test, Scholastic Aptitude Test (SAT) verbal and mathematics scores, total credit hours accumulated, GPA, high school rank, and program major field. He found that prior programming experience was moderately correlated with the final computer course grade (r=0.7). His predictor variables accounted for 60% of the variance of the course grade. Gray (1974) too, was interested in the guidance and counselling of post-secondary students. He identified and analyzed selected variables which included: age, sex, Revised Programmers Aptitude Test (RPAT) scores, ATPP scores, and scores on seven areas of the  Review of the Literature  Page 11  General Aptitude Test Battery (GATB). He found that Intelligence, Verbal, and Spatial scores on the GATB and ATPP scores were valid predictors of success in the data processing technology program, but none were found to be reliable enough to determine acceptance or rejection of prospective students. The ATPP score was determined to be the best single indicator of success, and was useful as a counselling guideline. Gray also concluded that regression equations can be used effectively to predict achievement level for placement purposes. Tillman (1974) conducted a study to predict successful computer programmers using the Biographical Information Blank (BIB), the School and College Abilities Test (SCAT) and the SVIB as predictor variables. The SCAT Math section demonstrated validity as a predictor of success; while the BIB and SVIB variables were not significantly correlated to success. Newsted (1975) used 14 independent variables in attempting to predict both grades in an introductory university programming course and students' self-perceived ability in this course. He found that college GPA was the only independent variable that correlated positively with the programming course grade. He found that programming experience and career orientation predicted self-perceived programming ability. None of the personality variables correlated significantly with either criterion variable. None of the personality variables entered into either of the regression equations with the two dependent variables. Of the behavioural variables the "time spent on the course and working with other students correlated negatively with the dependent variables" (p.89). That is, although poorer students may spend much time and ask many questions of their instructors and fellow students, it does not improve their grade or their ability perception. Furthermore the 14 predictor variables used in this study fail to account for the majority of the variance in either the course grade or the self-perceived ability variables (i.e. only for 41% and 49%, respectively).  Review of the Literature  Page 12  Capstick, Gordon and Salvadori (1975) investigated the use of ATPP scores as predictors of course grades in two different computing courses. The ATPP scores showed no significant relation to course grades in the COBOL course, but were significantly related to course grades in the FORTRAN course. The arithmetical reasoning component of the ATPP was the only significant component that could be used to predict grades in the FORTRAN course. Mazlack (1976) investigated the correlations between a student's gender, his academic program, his semester in school and his grade in the computing course. He found negligible correlations between each of these three independent variables and computer science achievement. He concluded that there is no reason to segregate students from differing semesters or from various academic programs because of concerns about differences in learning ability when these concerns are based on academic discipline bias. Over several years Mazlack (1980) found that academic discipline, sex and academic experience have low correlations with success in an introductory programming course. He also found that Programmer Aptitude Tests (PAT) had low predictive value when administered to people at college level. Mazlack concluded that the longer a student is in school the less uniform is his application to the study of the introductory computer science course. As in his later study, Mazlack (1976) concluded that those beginning their university experience and those well along in their academic careers seem to succeed or fail in much the same manner. Petersen (1976) gathered biographical information, temperament information from the Thurston Temperament Schedule, and general aptitude information from the GATB as independent variables in predicting achievement in the course "Introduction to Computers". His criterion variables were the midterm examination mark, the programming grade, the final examination score and the final grade. He concluded that the criterion variables were  Review of the Literature  Page 13  correlated with the independent biographical variables high school rank, the number of mathematics and science courses taken in high school, the number of semesters of mathematics and science courses taken in high school, high school GPA, college GPA, high school mathematics and science GPA, and a self-predicted grade in the introductory computer course. The criterion variables were found to be correlated with the temperament variables "impulsive" and "sociable". The dependent variables were correlated with the general aptitude variables "verbal", "numerical", "clerical", and "general intelligence". The college GPA although a "late" predictor variable was the best predictor for the programming grade, the final examination score and the course grade. That is, the students with the highest GPA also had the highest programming grades, the highest final exam scores and the best course grades. Later, Petersen and Howe (1979) extended this research. They were among the first to undertake to predict success in a "general education type of introductory computer course" (p. 183). Their data included fifteen items from an autobiographical survey, the Thurstone Temperament Schedule results, and the GATB scores, which included a general intelligence score. Of these predictors only college GPA and general intelligence were found to contribute significantly to predicting an introductory computer course grade. The authors state that in advising pre-college students on the "Introduction to Computers" course, "an able high school student successful in mathematics and science will probably be a successful student in computer science" (p. 190). Schroeder (1978) conducted a study to determine if spatial reasoning ability, mathematical reasoning ability and Piagetian formal thought ability were significantly correlated with achievement in computer programming classes. His results revealed that mathematical reasoning ability had the greatest correlation with computer programming course achievement. Further analysis indicated that of the three independent variables both mathematical reasoning and Piagetian formal reasoning scores made significant contributions to explain-  Review of the Literature  Page 14  ing the variance of achievement scores. Thus, he concluded that these two factors in combination are significant in predicting success in computer programming, but spatial reasoning ability is not important. Cheney (1980) studied the relationship between cognitive style and a student's ability to program. He found that analytic decision makers tend to perform better than heuristic decision makers on programming examinations. He concluded that cognitive style can be used as a predictor of student success in learning a computer programming language. Kurtz (1980) conducted a study to investigate the relationship between intellectual development and performance in an introductory programming class. He found that intellectual development did not vary with sex, class level, and previous coursework. He found that the "late concrete" level and the "late formal" level of intellectual development were strong predictors of "poor" and "outstanding" performance, respectively, in an introductory programming course. It was also found that the intellectual development level predicts performance on tests better than it predicts performance on student-written programs. Plog (1980) conducted a study to investigate the relationship of selected variables in predicting academic success in computer programming using community college introductory computer programming students. She found no significant relationship between total aptitude test scores and the degree of academic success in an introductory computer programming course. She found that both the verbal ability scores and the quantitative ability scores were higher for those students successfully completing the computer programming course than for those who were unsuccessful. She concluded that the SCAT total aptitude scores do equally well for both sexes in predicting final course grades in the introductory course. Older students have higher final course grades and higher total aptitude scores than younger students. Caucasians' scores were significantly higher than Negroes' scores on final course grades and total aptitude scores. She found that the amount of previous train-  Review of the Literature  Page 15  ing and experience in data processing made no significant difference in the total aptitude test scores and in the degree of academic success in an introductory computer programming course. Stephens, Wileman and Konvalina (1981) did a study to identify group differences in computer science aptitude of beginning computer science students at university. They used various student background factors which included age, sex, college year, hours worked, previous computer experience, estimated high school performance, and estimated current college performance, as well as aptitude variables which included reading comprehension, sequence completion, logical reasoning, algorithmic execution and alphanumeric translation, in order to investigate any group differences. However, of these factors only two of them had a significant impact on performance - the estimated current college performance and the estimated high school performance. "Students who performed well in high school and in college generally performed well on the aptitude pretest" (p.94). The components of the pretest that were most significant in differentiating students (i.e. sequences, logical reasoning and algorithmic execution) relate to the mathematical reasoning ability of the students. Thus, these researchers concur with the findings of Petersen and Howe (1979) that students who do well in high school mathematics and science in particular, do well in an introductory computer science course. A significant group difference was found on the algorithmic execution component of the pretest between age groups and between groups differing in previous computer experience. Older students, i.e. 35 or older, and those students with no prior computer experience performed at a lower level on the execution of algorithms. The reading comprehension component did not produce significant differences in scores for any of the factors. Thus, the authors claim that their research indicates that reading comprehension does not seem to be useful as a predictor of computer science aptitude.  Review of the Literature  Page 16  Wileman, Konvalina and Stephens (1981) used reading comprehension, alphabetic and numeric sequences, logical reasoning, algorithmic execution, alphanumeric translation, age, high school performance and the number of hours of work per week as factors in predicting success in a beginning university computer science course. When correlated with final exam scores for the computer science course, only reading comprehension, alphabetic and numeric sequence completion, logical reasoning, and algorithmic execution were found to be significant. This is a much stronger result than that obtained by Mazlack (1980) reported earlier in this chapter. Although these variables reinforce one another, they do not provide independent sources for prediction. In addition, a stepwise multiple regression was completed to determine the most important factors in predicting success. When all eight factors were included in the prediction equation only 25% of the variance in final exam scores was explained. Stephens, Wileman, Konvalina, and Teodoro (1985) found that there was a direct relationship between performance on this placement test and on computer course performance at a foreign university. Wileman, Stephens and Konvalina (1982) examined the relationships between mathematics competency and computer science achievement in order to advise and to place prospective computer science students. They concluded that there is a strong relationship between mathematical competencies and probable success in beginning computer science courses. Konvalina, Wileman, and Stephens (1983) compared students who withdraw from an introductory university computer science course with those who do not withdraw from these courses with respect to such demographic factors as age, estimated high school performance (Students chose among the categories "excelled", "above average", and "average or below".), hours worked per week, prior computer education, prior nonprogramming computer work experience, prior programming work experience, years of high school mathematics, and number of college mathematics courses, and with respect to such predictor factors as number and letter sequences, logic-type questions, calculator  Review of the Literature  Page 17  simulator, algorithmic questions, and high school algebra word problems. A consistent result that was found is the "important role of mathematical reasoning ability and mathematical background to potential success in computer science" (p.377). The nonwithdrawers were significantly older; their high school performance was significantly better, they had significantly more previous educational experience involving computer science; they had more mathematics background (high school and university combined). Between the two groups "there were no statistically significant differences in the number of hours worked per week, previous work experience involving computers, and the number of years of high school mathematics" (p.378). On the five predictor components the scores for the nonwithdrawers were significantly higher for the sequences, the logic-type questions, the calculator and word problems components, in addition to the predictor total. The authors are using the results of this research in advising and placing students. Fowler and Glorfeld (1981) have developed a model expected to yield a 75% correct classification rate of either high or low aptitude in an introductory university computer course (that is evenly divided between programming and other computer topics). They used personal, academic and aptitude variables as predictors, including sex, age, academic major, university year, number of mathematics courses, GPA, final grade in an introductory computer science course, the SAT mathematics and verbal scores, and the Wolfe Programming Aptitude Test (WPAT) percentile scores. Of these factors the authors found that "Grade Point Average (GPA) is the most important single variable in the model, indicating that a student's past academic performance is the best indicator of future academic performance" (p. 108). Also, they found that the development of problem-solving skills is important to success in computing as indicated by the inclusion of the SAT mathematics score and the number of mathematics courses variables. They found that age is of marginal importance but younger students can be predicted to have a greater likelihood of success in introductory computing. One reason the authors suggest for this is "it is also possible that  Review of the Literature  Page 18  the younger students have had more exposure to computing in their secondary education" (p. 108). Glorfeld and Fowler (1982) followed up their previous study (Fowler and Glorfeld, 1981) with a validation study of the classification model developed earlier. The results of this second study reveal that an approximate 75% correct classification rate would be expected in actual application of the new model developed from the combined sample data set. The authors state that the age variable does not appear to be making a real contribution to the model. However, GPA is seen as the most important component of the model. The inclusion of the number of mathematics courses and the SAT mathematics score as predictors agrees with the findings of Alspaugh (1972), Petersen and Howe (1979) and Stephens, Wileman and Konvalina (1981), that mathematics background is related to the likelihood of success in introductory computing. Lemos (1981) performed an experiment using a pretest score and age as covariates which compared scores on an examination that tested proficiency with BASIC language between university students who were business majors and others who were not. All of these students were enrolled in an introductory computer course. The results showed no significant difference between the scores of the two groups. The ability to understand programming languages is independent of the student's academic direction. "Thus, the assertion that the general university student will not do well if programming is stressed does not appear to have an empirical basis" (p. 157). Leeper and Silver (1982) developed a predictor of success in an introductory programming course for use as a screening instrument to restrict enrollment. They used the number of units of each of high school English, mathematics, science, and foreign language completed, the student's high school rank and the SAT verbal and mathematics scores and predictor variables, the SAT verbal and mathematics scores correlated the strongest with  Review of the Literature  Page 19  the course grade. All of these independent variables explained at most 26% of the variation in the grades. Approximately 64% of those students for whom failure was predicted did fail. Barker and Unger (1983) shortened the Kurtz (1980) test of abstract reasoning to use it to predict success in an introductory computer science class and to segregate students into accelerated and non-accelerated sections of an introductory computing course. The results failed to yield the significant correlation between intellectual development predictor and final course grade as Kurtz (1980) found (0.12 versus 0.80 for Kurtz). However, this tool successfully segregated the advanced students from average and below average students. Nowaczyk (1983) used factors which included past academic performance, personality factors and performance in problem solving to predict success of college students in introductory computer prograiriming courses. He found that past academic performance in mathematics and English courses, the amount of previous computer experience, the expected grade in the course, and performance on selected logic and algebraic word problems were significantly related to course performance. Stevens (1983) found a significant difference between cognitive styles and academic success of education students enrolled in instructional computer courses. Field independent students (those whose "perceptions are analytical") had significantly higher achievement scores than field dependent students (those whose "activities and perceptions are global and tend to focus on the total environment") in the computer courses (p. 229). She concludes that educators must acquire new and different skills to meet the needs of students. Hostetler (1983) examined to what extent certain cognitive skills, personality variables, and past academic achievement can be used to predict success in an introductory computer programming course. Of the 20 independent variables used only the diagramming and  Review of the Literature  Page 20  reasoning tests of the CPAB and a student's GPA correlate significantly with a student's final course score. No personality trait variables and mathematics background correlated significantly with the dependent variable. A multiple regression equation of the five highest correlating predictor variables explained approximately 43% of the variance in the final course score. Sorge and Wark (1984) conducted a study to determine which factors can be used to predict success or lack of success as a computer science major. As predictor variables they considered such precollege data as SAT verbal and mathematics scores, number of semesters and average high school grades in mathematics, science, English, and high school rank. The authors found that "to have a reasonable chance of making satisfactory progress" (p.44) in the computer science majors program students should have significant mathematical and verbal skills (SAT mathematics score of at least 560 and SAT verbal score of at least 500). They observed a high attrition rate even by better students. These dropouts said that time, attention to detail, systematic thinking and the impersonal interaction with machines were undesirable activities of the program. The authors conclude that there are factors other than academic ability involved in being successful as a computer science major. Campbell and McCabe (1984) were concerned with identifying factors that influence success in the first year of a computer science major. They used the SAT mathematics and SAT verbal scores, the rank in high school graduating class, the size of high school graduating class, the number of semesters of each of high school mathematics, science and English courses, the average grades in each of these three high school subject areas, and sex as the predictor variables. They found that the SAT mathematics score, the average grade in high school mathematics and sex were the best predictors of success.  Review of the Literature  Page 21  Whipkey and Stephens (1984) used SAT verbal and mathematics scores, a Quality Point Average (QPA), the Myers-Briggs Type Indicator (MBTI) scores of ExtroversionIntroversion, Sensing-Intuition, Thinking-Feeling, and Judgement-Perception to predict beginning programming success. They found that the QPA was correlated the highest (r=0.73) with the target variable, a programming ability score (PAS). The best statistically significant predictor model uses QPA and SAT mathematics scores (r=0.75) to predict PAS. The personality traits measured by MBTI failed to add significantly as predictors. Butcher and Muth (1985) performed a study to predict college performance in introductory computer science courses using only high school transcript data and American College Testing Program (ACT) test scores. They found that performance can be predicted based on this information. However, only 37% of the variation in final course grade was accounted for by this relationship. Szymczuk and Frerichs (1985) investigated the predictive value of several standardized aptitude instruments for use at the high school level. They administrated the Iowa Test of Educational Development (ITED), Science Research Associates' CPAB and the WPAT. They concluded that mathematical ability is an important factor influencing success in a secondary school computer science course. Werth (1986) studied the relationship between the student's grade in introductory computer science and his/her sex, age, high school and college academic performance, number of mathematics courses, work experience, cognitive development, cognitive style, and personality factors. She used the measure developed by Kurtz (1980) to measure intellectual development, the Group Embedded Figures Test (GEFT) as a measure of cognitive style, and the MBTI as a measure of personality type. Significant relationships were found between letter grade and college grades, the number of hours worked, the number of high school mathematics courses, the GEFT scores, and the Piagetian intellectual development  Review of the Literature  Page 22  measure scores. Although no relationship was found between grade and personality type, computer science students "were found to be far more introvertive, intuitive and thinking than the population as a whole" (p. 141). Dey and Mand (1986) investigated the relationship between the level of the student's mathematics background and his performance in introductory BASIC, Pascal, and COBOL courses, and the effect of the student's performance in one computer language course on the learning of another. Their results indicated significant correlations between a respondent's reported average grade in high school and college mathematics courses and his expected grade in a current introductory computer science course. They found that students with some computer language background are more likely to succeed in a subsequent computer science course than students who have had no previous computer language exposure. For students exposed to more than one computer language, they found that there was a stronger complementary relationship between COBOL and Pascal than between either BASIC and COBOL or BASIC and Pascal. That is, students with prior exposure to COBOL or Pascal had higher grade expectations in a subsequent Pascal or COBOL course than students with no prior computer language experience. Whereas students with prior exposure to BASIC seemed to have no higher grade expectation in a subsequent Pascal or COBOL course than students with no prior computer language experiences. Greer (1986) examined the relationship between high school computer science course experience and subsequent introductory university computer science course achievement. The students were grouped according to both the amount of high school computer science instruction and the degree of emphasis on structured programming in high school courses. No significant differences in overall achievement in the university course were found among the groups. Students with more high school experience were found to be less likely to withdraw from the course. Greer concluded that higher grades in university computer science do not result from high school computer science experience.  Review of the Literature  Page 23  Oman (1986) investigated to what extent previous computer experience (the number of time-sharing systems previously used, the number of microcomputers previously used, the number of programming languages previously used), the number of years since high school graduation, and SAT mathematics and verbal proficiency scores have in predicting how successful a person will be in an introductory computer science course. Mathematics proficiency was the most highly correlated to the dependent variable, final course grade. The SAT mathematics score alone accounted for 65% of the grade variation. All six independent variables accounted for 82% of grade variation. The author concludes that this model can be used as an advising tool for predicting success in introductory computer science courses. Gathers (1986) was concerned with the identification and use of factors which could effectively predict success in a first computer science course in university. His study included as factors: high school GPA, ACT scores, standardized Nelson Denny Reading scores, and the University of Tennessee at Martin (UTM) Math Placement Test score. Of these placement factors the ACT English score and the UTM Math Placement Test score were found to be the best predictors of success in the first course for computer science majors. This research indicated that verbal skills are just as important as mathematical skills in predicting success. Ramberg and Van Caster (1986) examined the differences in scores on a placement test [the same test used as in Konvalina, Wileman, and Stephens, (1983)] between a group of students who eventually withdrew from an entry-level programming course and a group who finished the course. They found that those students with prior programming experience scored higher on the placement examination than both those with no prior programming work and those who did not finish the course. The amount of mathematics background was directly related to the placement examination score of the finishers. The authors inter-  viewed many "successful" students who attributed almost unanimously their good grade to having enrolled in a beginning computer programming class in high school. Sauter (1986) assessed the relative roles of mathematics aptitude and language aptitude in the development and enhancement of programming skill. In an introductory COBOL course she found that programming ability is related to both mathematical aptitude and language aptitude. However, only 20% and 17% of the variation in students' performances on a Syntax Test and a Logic Test, respectively, could be explained. Cafolla (1987) conducted a study to deterrnine if success in writing computer programs is related to the level of cognitive development, verbal reasoning, mathematical reasoning, and grade point average. He found, using multiple regression analysis, that the level of cognitive development and verbal reasoning were significant predictors of success in computer programming. The best single predictor was verbal reasoning (r=0.62). Dixon (1987) investigated factors which may contribute to success or failure in university computer science courses. She found no significant differences in achievement for those who started computing studies prior to high school, in high school, and at college. A significant positive correlation was detected between SAT mathematics scores and computer science course achievement. A positive correlation existed between achievement and the length of programs written. Dixon discovered no significant differences in achievement between students with different initial programming language experience. She found no significant differences in learning style preference and achievement. However, Dixon did detect significant differences in learning style preference for students selecting computer science as a major compared to those students not majoring in computer science. McGee, Polychronopoulos and Wilson (1987) investigated the influence of BASIC language knowledge on performance in two introductory Pascal language courses. In one course no differences in final course grades were found based on prior programming lan-  Review of the Literature  Page 25  guage background. In the other course students who had done prior programming in BASIC had significantly higher final grades that those who had no programming background. Sharma (1987) analyzed many research studies concerning the identification of components of computer science aptitude responsible for success in computer science courses. He was interested in whether cognitive styles, psychological types, and background variables are factors which in addition to mathematical ability may have a bearing on programming ability. The background variables include academic program, semester in school, gender, college GPA, and general intelligence tests. Sharma agrees with Stevens' (1983) finding that the learners' cognitive styles and psychological characteristics should be considered in the planning of programs and strategies to maximize their academic success in computer science courses. Further research is required to investigate the relationship between both cognitive styles and psychological types and performance in computer science courses. Howerton (1988) administered a post-treatment knowledge retention test to students who had completed an introductory college computer programming course. He was attempting to determine whether any of three levels of pre-college exposure to computers and computing would have a significant effect on student performance in this course. He found that for all three levels of pre-college computing exposure the mean scores of those who had been exposed were significantly higher than the mean scores of those who had not been exposed. He concluded that students with pre-college exposure to computers and computing at any level can be expected to achieve higher scores in introductory computer programming courses than students who have not had that exposure. In general these studies indicate: 1) that students with a strong background in mathematics and science are more likely to perform well in introductory computer science courses (Alspaugh, 1970 and 1972;  Review of the Literature  Page 26  Petersen, 1976; Schroeder, 1978; Petersen and Howe, 1979; Stephens, Wileman and Konvalina, 1981; Konvalina, Wileman and Stephens, 1983; Fowler and Glorfeld, 1981; Glorfeld and Fowler, 1982; Nowaczyk, 1983; Campbell and McCabe, 1984; Szymczuk and Frerichs, 1985; Dey and Mand, 1986; Gathers, 1986; Ramberg and Van Caster, 1986; Werth, 1986; Dixon, 1987; Sharma, 1987), 2) that students' GPA is the best single predictor of an introductory computer science course grade, of the many variables studied (Bauer, Mehrens and Vinsonhaler, 1968; Correnti, 1969; Buff, 1972; Newsted, 1975; Petersen, 1976; Petersen and Howe, 1979; Fowler and Glorfeld, 1981; Hostetler, 1983), and 3) that programming aptitude scores can be used in predicting programming success (Correnti, 1969; Mussio and Wahlstrom, 1971; Bateman, 1973; Hunt and Randhawa, 1973; Gray, 1974; Tillman, 1974; Capstick, Gordon and Salvadori, 1975; Plog, 1980; Leeper and Silver, 1982; Hostetler, 1983; Sorge and Wark, 1984; Campbell and McCabe, 1984; Whipkey and Stephens, 1984; Szymczuk and Frerichs, 1985; Oman, 1986; Sauter, 1986). Other studies have included many demographic variables that proved to be somewhat significant in predicting computer course success. Correnti (1969) included study habits and attitudes and the occupational level of the parents as independent variables. Bateman (1973), Nowaczyk (1983), Dey and Mand (1986), Oman(1986), Ramberg and Van Caster (1986), McGee, Polychronopoulos and Wilson (1987) and Howerton (1988) successfully used previous computer experience. In his investigation of cognitive style Cheney (1980) found that analytic decision makers perform better than heuristic decision makers on programming exams. Kurtz (1980) found that intellectual development predicted performance. Stevens (1983) concluded that field independent students had significandy higher scores in computer courses than field dependent students. Campbell and McCabe (1984) concluded that SAT verbal scores and gender were predictors of performance in computer science. Werth (1986) found that the amount of work experience had a significant correlation with computer science course grade. Cafolla (1987) used cognitive development and verbal reasoning as predictor variables. However, demographic variables have not proved to be  Review of the Literature  Page 27  as good in predicting computer science success as have the academic or aptitude variables. Tillman (1974) learned that biographical and vocational interest variables were not correlated to successful programming. Newsted (1975), Hostetler (1983), Whipkey and Stephens (1984) and Werth (1986) found that none of the personality variables used correlated with programming grades. Plog (1980) found previous data processing training and experience were not significant in predicting success in a computer programming course. Mazlack (1976 and 1980) concluded that a student's sex, and his academic program, his semester in school have negligible correlations with computer science course achievement. Lemos (1981) ascertained that student's academic discipline was independent of his ability to understand programming languages. Barker and Unger (1983) learned that intellectual development did not have a significant correlation with the final course grade in an introductory computer science class. Greer (1986) concluded that the amount of high school computer science course experience has no effect on computer science grades at university. Sharma (1987) suggested that further study is required to determine what effect cognitive styles and psychological characteristics have on performance in computer science courses. Predicting Achievement  in  General  Many studies have been conducted to predict academic performance in general from various factors for student counselling purposes (e.g.: Cronbach, 1949; Horst, 1957; Lavin, 1965; Fudge, 1970; Hunt, 1977). College level ability tests correlate about 0.50 to 0.55 with GPA (Cronbach, 1949). However numerous personality and social factors are also involved and should not be overlooked in academic performance prediction. The content of predictor batteries varies depending on the particular course for which grades are being predicted (Horst, 1957). However, Cronbach (1949) contradicts this  Review of the Literature  Page 28  stating that multifactor tests of abilities add litde to the prediction of performance in particular courses beyond what the general intelligence factor will predict. Lavin (1965) states that the "single best predictor of performance on the college level is the high school academic record" (p.57). Biographical information greatly aids in accurate prediction of academic performance in college (Fudge, 1970). These data, either taken alone or in combination with aptitude test scores, provide better estimates of academic performance than do aptitude test data, either taken alone or in combination with high school rank. The prediction of academic performance is a complicated exercise involving many factors, whether the task be to predict success in computer science or in some other subject area. If one is to develop in students an ability to adjust to the world of work one should be concerned with other outcomes besides academic achievement. Lavin (1965) says that "we need to develop additional dimensions of student performance and to find out how they relate to various aspects of life after the completion of school" (p. 168). These dimensions will have implications for what one should include in a computer science curriculum.  Introductory Computer Science Curriculum What should be taught in an introductory computer science course? Up to the present, the "typical" introductory course teaches "the students to become proficient in a single programming language" (van Dam et al, 1974, p. 175). Furugori and Jalics (1977) presented the results of a questionnaire returned from 44 universities. They found similar results to those found by van Dam's survey. "The first course in Computer Science is definitely a programming course for almost all the schools" (Furugori and Jalics, 1977, p.l 19). Brookshear (1985) stated that this traditional approach of learning to program in the first year and later learning other subjects, produces students with a narrow, vocationally-  Review of the Literature  Page 29  oriented view of computer science. The first course in computer science should not give "the unfortunate impression that Computer Science = Programming" (Ralston, 1984, p. 1002). As for the future, many suggestions have been made as to what should be included in the curriculum for an introductory computer science course (Austing and Engel, 1973; Austing et al, 1978; Bork, 1982; Cashman and Mein, 1975; Cherniak, 1976; Gibbs, 1977; Gries, 1974; Hyde, Gay and Utter, 1979; Kimura, 1979; Poirot, 1976; Ralston, 1984; Ralston and Shaw, 1980; Schneider, 1978; Singhania, 1980; Solntseff, 1978;"Task Force", 1985). Computer science has developed so much during the past twenty-five years that people are keen to examine its future development, which is almost certain to lead away from the present state within the next few years. Problem solving is a necessary skill for introductory computer scientists (Cashman and Mein, 1975; Grady and Gawronski, 1983; Gries, 1974; Hyde, Gay and Utter, 1979; Poirot, 1976; Solntseff, 1978). This skill should be taught early in the introductory course preceding computer programming (Cashman and Mein, 1975). Poirot (1976) stated that in one of his eight areas of study "the student's requirements as to problem-solving ability and programming understanding far exceed his need for proficiency in a particular language. ... problem-solving approaches should be language independent" (p.45). Teachers should emphasize problem solving and algorithm development techniques that are applicable regardless of the programming language (Grady and Gawronski, 1983). Hyde, Gay and Utter (1979) reported that an important ingredient that contributes to the success of their introductory computer course is "the utilization of a simple-to-learn interactive language (BASIC) at the beginning of the course, which allows the instructor to concentrate on teaching PSP [problem solving process] and allows the students to solve problems on the computer early in the course" (p.58).  Review of the Literature  Page 30  Algorithmic solutions to problems should also be taught in the introductory course (Gries, 1974; Schneider, 1978; Solntseff, 1978). Schneider (1978) believed "the single most important concept in a programming course is the concept of an algorithm" (p.108). Other important ideas worth inclusion in an introductory course involve the student developing programming reading skills (Kimura, 1979) and using previously written programs (Cherniak, 1976) before being taught program writing skills. A first year computer science course needs more discrete mathematics content (Ralston, 1984). Mathematical reasoning ability and mathematical maturity are necessary for success in computer science (Ralston and Shaw, 1980). They explained that because computer science is an evolving field, specific skills learned today will soon become obsolete. However, with a knowledge of the principles that underlie these skills, a student will be free from obsolescence. In secondary school computing a broader course designed to meet the needs of all students is required, since all students will be affected by the computer (Crawford, 1978). Singhania (1980) agreed with this in his college business computing science curriculum course. Students enter this course with a tremendous variation in background, motivation, expectations and analytic skills. Thus no single emphasis will likely meet the needs of the majority of students. Self (1983) stated that programming skills should be taught as one of the five main parts of an ideal computer literacy course to children in order to narrow the socioeconomic spread between them and to give the students a higher level of self esteem and accomplishment. He said also, that skills learned in programming have carry-over value into other academic areas. Using the results of a survey involving returns from over 3500 Minnesota secondary school teachers nearly 1200 of which use computers in their classrooms, Hansen, Klassen, Anderson and Johnson (1981) have reported information on what teachers think every high  Review of the Literature  Page 31  school graduate should know about computers. The majority of the teachers (nearly 84%) felt that "every secondary school student should have some minimal understanding of computers" (p.468), and (nearly 93%) that "every secondary school student should learn about the role that computers play in our society" (p.468). However there was a lack of consensus among teachers on the third and final statement, where just under 29% agreed that "every secondary school student should be able to write a simple program" (p.468). Solntseff (1978) stated that an introductory course should provide a firm foundation on which the student can build his understanding of the computer as a problem-solving tool. "Probably the most important objective of an introductory programming course should be the development of positive attitudinal outcomes in students" (Lemos, 1978, p.301). Lemos used some team activities within the class in order more effectively to achieve this objective. Cole and Hannafin (1983) found that high school students often choose to enroll, or not to enroll, in introductory computer science based on inaccurate perceptions of the course. More attention needs to be directed to the clarification of the expectations of the introductory computer science coursework. Among its many recommendations a task force working under the Association for Computing Machinery (A.C.M.) proposed that there be four courses offered at the secondary level: two full year courses offering an introduction to computer science, and two half year general education courses. One of these courses is to be about programming; the other, to include information about the ways computers are used and their impact on people's lives ("Task Force", 1985). These ideas must be given serious consideration when revisions are made of existing introductory computer science courses at secondary and post-secondary levels. These courses should be in a constant review process because computer technology is ever-changing.  Page 32  Review of the Literature  Introductory  Computer  Programming  Language  There is considerable debate about which computer programming language should be taught in an introductory computer science course (Bauer, 1979; Blaisdell and Burroughs, 1985; Bork, 1971; Braswell and Wadkins, 1984; Brookshear, 1985; Chanon, 1977; Charmonman and Ralston, 1975; Cherniak, 1976; Citron, 1983; Curtis, 1983; Lemos, 1979; Lucas and Kaplan, 1976; Papert, 1980; Poirot, 1979; Schneider, 1978; Self, 1983; Solntseff, 1978; "Task Force", 1985; Ulloa, 1980; Van de Riet, 1975; Williams, 1982). Charmonman and Ralston (1975) believed that "language ... is important but not nearly so important as some leading computer scientists would have you believe" (p.965). They also wrote that "the language used for teaching in a first course in computer science is much less important than how the course is taught" (p.965). Brookshear (1985) wrote that "teaching programming is more general than teaching a programming language. The former is to teach students organizational skills while the latter is to teach a particular way of organizing" (p.24). The organizational skills taught do not dictate the use of a particular programming language. One of the major problems in teaching programming is trying to implant good programming habits in students (Ulloa, 1980). However, "no matter how careful one is to introduce clean programming habits (restricted control flow, top-down design, meaningful data names, completeness in specifications and documentation, etc.) at the very beginning of students' programming experience students too easily fall into 'quick and dirty' programming" (Cherniak, 1976, p.65). One language that is highly acclaimed as an excellent introductory programming language is Pascal, a well-structured, high-level language. (Pascal is used in introductory computer science courses at the University of British Columbia - CPSC 114, CPSC 116, CPSC 118, and CPSC 210.) After using Pascal in an introductory computing course Bauer (1979) said that his students were able to do longer and more interesting assignments than those who  Review of the Literature  Page 33  completed the course prior to the introduction of Pascal. He stated that students have no greater difficulty in learning Pascal than any other introductory language. Schneider (1978) had a set of ten basic principles which formed the skeletal outline of an introductory programming course. One of his principles was to "choose a programming language that enhances the learning process" (p. 109). It must be both rich in those constructs needed for introducing fundamental programming concepts, and simple enough to be learned in an introductory course. Schneider felt that Pascal best achieves both of these objectives. Since Pascal was specifically designed for use in the teaching environment, it "is probably the strongest contender for the title of 'state-of-the-art pedagogic language'" (Solntseff, 1978, pp. 122-123). He wrote that because Pascal is a good data and action structuring language (according to him BASIC, FORTRAN, PL/1 are not), it is also usable in higher level courses. Pascal has been selected as the language for the Advanced Placement (AP) program Computer Science course. The test development committee felt that it was important for the language of the AP Computer Science course to have "characteristics that facilitate structured prograniming and a high degree of modularity" (Braswell and Wadkins, 1984, p.10). Another language that is widely used, especially with microcomputers, is BASIC. It is extremely popular as the programming languagefirst-taughtat the secondary school level, both here in British Columbia, and across the continent. The National Science Foundation (N.S.F.) in a report based on the results of a survey, entitled "Computing Activities in Secondary Education" (C.A.S.E.), stated that in 1975, 62.4% of the secondary schools teaching computer science used BASIC as the programming language. "With the recent microcomputer development, this percentage has certainly increased" (Poirot, 1979,  Review of the Literature  Page 34  p. 103). (The writer would concur with Poirot now, ten years later.) According to Self (1983), BASIC is becoming "the universal language of the masses" (p.9). He felt that "Because of its popularity, BASIC is likely to survive until programming in the English language becomes commonly available" (p.9). Papert (1980) (author of the LOGO language) claimed that BASIC language is synonymous with programming "despite the existence of other computer languages that are demonstrably easier to learn and are richer in the intellectual benefits that can come from learning them" (p.34). Some leading computer scientists are opposed to the teaching and learning of BASIC language. "Informally a great many people in computer science departments will tell you that it (BASIC) is a disaster." (Bork, 1983b). Atherton (1982b) wrote that BASIC has "serious deficiencies which have become painfully apparent" (p.9). Bork (1982b) said "Many of the people learning to program in junior high school and high school cannot overcome the initial bad habits which have often been instilled in them when they come to the universities... The main culprit is BASIC" (p. 12). "Many universities now recognize that students who learned BASIC programming in high school have a difficult time mastering the computer science curriculum" (Bork, 1985, p.31). He added that as in many other pursuits "If students are introduced to programming initially in such a way as to build up a set of bad habits associated with poor programming style, it is very difficult or even impossible to change these bad habits at a later stage " (Bork, 1983a, p.24). The Computer Science Department of the United States Air Force Academy concurred with Bork that "students who have first learned BASIC find it more difficult to learn structured languages such as Pascal" (McCann, 1983). Thus, it is critical that the proper habits be established at the outset. Bork (1983a) stated that "if students have not seen correct prograrnming style, they perhaps will not be able to continue with computers at the college level" (p.27). One might ask what is the "correct" programming style? According to Bork, the language must be "structured". He said that it is impossible to structure some versions of BASIC. He  Review of the Literature  Page 35  claimed that BASIC has no adequate procedures; the single letter or single letter plus single digit used for variables in some versions of BASIC prevents one from giving meaningful names to variables, which makes later program revision very difficult. Atherton (1982a) described several examples of poor features of BASIC~"spaghetti-like" structure, multiple statement lines and short variable names. Papert (1980) stated that BASIC programs acquire the structure of a labyrinth and only the brightest children are able to write nontrivial programs. Dijkstra (1968a) wrote a powerful letter to the editor on the "Go To" statement entitled "Go To Statement Considered Harmful". He stated that the "quality of programmers is a decreasing function of the density of 'go to' statements in the programs they produce" (p. 147). Dijkstra said the "use of the 'go to' statement has such disastrous effects,... the 'go to' statement should be abolished from all 'higher level' programming languages" (p. 147). BASIC language does have a "Go To" statement. In structured programming the "Go To" statement is considered to be superfluous. Dijkstra wrote further that "The 'go to' statement as it stands is just too primitive; it is too much an invitation to make a mess of one's program" (p. 147). Why then would BASIC have incorporated the "Go To" statement? Since BASIC was developed at a time when there was little experience with programming particularly for large, complex activities a "number of rules have been discovered, violation of which will either seriously impair or totally destroy the intellectual manageability of the program" (Dijkstra, 1972, p. 863). Dey and Mand (1986) wrote that there is "a widely held belief that BASIC, because of its lack of structured constructs, does not contribute significantly to learning in other computer science courses, and may in fact be detrimental" (p. 146). Some computer scientists feel that learning an unstructured language, such as BASIC, "permanently affects an individual's ability to learn a structured language; these computer scientists recommend that no unstructured languages be taught" ("Task Force," 1985, p.272).  Review of the Literature  Page 36  A second, and perhaps more important, reason why some leading educators in this field hold B A S I C language in disfavour is because of the methods used in teaching the language. Bork (1982b) said "The major problem is the way programming is taught" (p. 12). Bork (1983a) claimed that "only a very small percentage of courses in B A S I C are taught so as to promote good habits" (p.27). He (Bork, 1982b) wrote that in most instances B A S I C is taught antithetically to everything else known about programming. The process of programming is very different from what it was twenty five years ago. Bork (1983a) stated that many high school courses are not teaching programming as it is being practised today, but are teaching strategies which have long since been discredited for practical programming. He believed that students should be taught more modern techniques. Bork (1987) wrote that B A S I C can be taught in structured fashion, but it is almost never done. Christensen (1982b) stated "that as a programming language B A S I C is hopelessly obsolete. It never was a very good one, but seen from a modem point of view it is a disaster. People who start to learn programming using B A S I C may easily be led astray and after some time, find themselves fighting with problems that could be solved with almost no effort if they used a programming language more suited to guiding human thinking" (p.7). Carney (1983) wrote that BASIC'S poor reputation "is based on teachers who have no formal background in computer science and are simply following the obsolete programming style in B A S I C that is not modularized, uses too many GOTO's, and is not at all consistent with the structured programming languages" (p.8).  A third reason to avoid B A S I C language in the minds of many of the experts, is BASIC'S lack of standardization. Bork (1982a) stated that there is no widespread move to have existing B A S I C s or newly developed B A S I C s conform to a standardized version of the language, which is now underway. The standard version has departed greatly from the current implementations according to Bork. He added that B A S I C is one of the least standardized computer languages.  Review of the Literature  Page 37  As an alternative to BASIC programming language Bork (1982b) has suggested LOGO or Pascal or another structured programming language. "In the earliest exposures to programming it is important to work with a structured approach, a language which encourages structured use" (Bork, 1983a, p.27). Bork added "any programming course should focus on the issues of style and structure, and these should receive greater attention than any issues of grammar" (p.27). In conclusion the three reasons outlined by Bork (1982a) why the teaching of BASIC must stop are: 1. BASIC "does not lead easily to structured programming (and) tends to develop poor programming habits", (p.33) 2. "there are better languages for the student to begin with", (p.33) and 3. "the lack of standardization in existing BASICs" (p.33). For these reasons Bork (1985b) wrote that "the better universities, particularly in courses for computer science majors, avoid BASIC 'like the plague'" (p.28). Bork's condemnation of BASIC as the "junk food of modem programming... (and) junk food tends to destroy the body's desire for better types of food... (so) STOP TEACHING BASIC" (1982b, p. 12) has caused the writer to believe that some study is required to investigate the differences in achievement in computer science courses between those students who have some prior knowledge of BASIC and those who do not have any BASIC knowledge. Williams (1982) suggested that perhaps a language such as BPL ("BASIC-Pascal-Liaison, or Better Programming Language or simply a conversational programming language which was designed after APL" (p.289)) which was developed from the two languages BASIC and Pascal would be the best language to use in an introductory course. This two-tiered language which combines the advantages of BASIC and Pascal could be taught effectively  Review of the Literature  Page 38  to both the computer science student and the non-computer science student enrolled in the same introductory course. Lemos (1979) summarized ten approaches to programming language instruction. He added that all of these approaches lack empirical evidence of their pedagogical effectiveness and need to be tested in a wide range of experimental settings. One method of instruction is structured programming, - a key feature of Pascal (Dahl, Dijkstra and Hoare,1972; Wirth, 1973). "Although there seems to be a consensus that structured programming represents an improvement in programiriing practices, there also seems to be very little empirical evidence suggesting significant improvement in terms of programmer productivity, number or types of errors made, quality of the finished program, ease of learning, or ease of teaching. This is especially true for teaching programming at the college level" (Lemos, 1979, p. 175). The Association for Computing Machinery Task Force (1985) recommended that the specific language used in a first computer science course should encourage and facilitate modular, structured programming. Pascal language or Structured BASIC language could be used. On the other hand, Lucas and Kaplan (1976) demonstrated with an experiment that structured programs were easier to modify. "Well-structured programming is closely linked to well-structured problem solving" (Van de Riet, 1975, p.954). Citron (1983) claims that the teaching of problem solving is more important than the actual computer language being taught. She said that structured programming is possible with many different languages, including BASIC. Hall and Kidman (1975) claim that "there is a great need to teach student programmers working in such languages to be consciously concerned with the structure and organization of their programs" (p.313). However, just because structured programming may be preferable does not mean that it is best introduced in a beginning course (Klein, 1983). Beginners do not need to be encumbered with the complexity required of Pascal language (Blaisdell and Burroughs, 1985).  Review of the Literature  Page 39  There appears to be no agreement among those teaching the first course as to the best introductory computer language. It is possible that every programming language is being taught somewhere in an introductory computer course. A number of criteria must be met in selecting the language to be used in an introductory computing course (Van de Riet, 1975). The application to which the programming language is being used is more important in determining its appropriateness, than what language is being used (Blaisdell and Burroughs, 1985). There are advantages for a student to learn two or more contrasting programming languages in an introductory course (Bork, 1971). Learning more than one language gives the student additional insight into programming not afforded with knowledge of just a single language. Bork said "when a student is familiar with only one language, he does not understand that language as fully as he might because he has nothing to contrast it with" (p.4). With the knowledge of two computer languages a student is able to choose a particular language for a particular problem (Bauer, 1979). The student can compare languages meaningfully. Learning two computer languages in an introductory course helps a student dispel the belief held by many students who have learned only one language, that the language he has learned "constitutes the 'center' of the computing universe" (Chanon, 1977, p.42). Whatever curricula and programming language(s) are taught in introductory computer science courses, it is desirable that instructors at secondary and university levels maintain close contact with one another and be in continuous communication with prospective employers (Austing et al, 1979). Certainly, the content and language(s) taught are worthy of a great deal of attention in our ever-changing technological society. Whether or not the syllabus and programming language taught in an introductory computer science course will affect the future achievement of students needs further investigation. For this reason this study proposes to attempt to determine the effects on student achievement of various programming languages used in introductory courses.  Page 40  Chapter Three  Methodology  The present study was conducted in order to determine how the knowledge of BASIC programming language affected the learning of further computer science material including other computer languages. To obtain the results, fifteen questions were asked. To answer these questions, data had to be gathered. Chapter Three consists of a discussion of the data collected and the procedures used to collect that data. The discussion is organized around the sample, the courses, the questionnaire, the pilot testing, the course grades, the fifteen questions asked, and the methods of analysis. Sample  Selection  The decision was made to survey first and second year computer science courses. By including both years, comparisons could be made between students with similar backgrounds in a first year course and those in their second university course. Some computer science instructors have suggested that differences between groups of students would be most apparent at the introductory level. The advantages or disadvantages of having previously learned BASIC would likely diminish with each successive computer science course. A questionnaire was distributed to students enrolled at U. B. C. in first or second year Computer Science courses (numbered CPSC 101, 114, 118,151, 210), or in a Computer Education course (CSED 217) during a class session held in the second week of the course. Table 1 shows the number of sections of each course surveyed.  Methodology  Page 41  Table 1 Number of Sections Surveyed Term  CPSC  1 2  217 2 1  Total  3  CPSC 101  CPSC 114  CPSC 118  CPSC 151  CSED 210  3 1  1 1  1 1  1 1  4 1  4  2  2  2  5  The questionnaire was distributed by the writer, who provided a brief oral explanation. Completed forms were collected immediately. The questionnaire took less than five minutes to complete, and most students required only two or three minutes for this task. The length of time required to complete the form depended largely on how many computer languages the student had previously learned. Table 2 shows the number of questionnaires returned from each course surveyed: Table 2 Number of Responses to the Questionnaire Course CPSC 101 CPSC 114 CPSC 118 CPSC 151 CPSC 210 CSED 217  Number of Responses 372 371 127 251 109 72  Total Course  1302 Descriptions  The courses in computer science at U.B.C. that were surveyed were one-term in length. A term course was comprised of approximately 37 hours of lectures and 13 hours of tutorials. In Figure 1 is a description of courses involved:  Page 42  Methodology  Figure 1 Courses Involved CPSC 101 Introduction to FORTRAN Programming - Practical introduction to computer use. Aspects of the FORTRAN language and some common algorithms and applications. Students will compose and implement several programs. Programming style will be emphasized. Students wanting a more comprehensive introduction to Computer Science should take Computer Science 114 and 116. CPSC 114 Principles of Computer Programming I - An introduction to the structure and use of digital computers. Concepts of algorithm, program and programming. Principles of program design using Pascal on terminals. Students will compose and implement several programs. In these exercises, emphasis will be placed on clarity and orderly development. CPSC 118 Principles of Computer Programming - Accelerated version of Computer Science 114 and 116, assuming some prior knowledge and experience of computer programming. Systematic study of structured programming in Pascal; data representation, algorithm design, introduction to computer organization. Omits introduction to FORTRAN. Students having previous experience with Pascal should take Computer Science 116 instead of 118. (Note: Computer Science 114 is a prerequisite for Computer Science 116.) CPSC 151 Introduction to FORTRAN Programming - A practical introduction to computer use. Aspects of structured FORTRAN and some common algorithms and applications. Introduction to Michigan Terminal System (MTS). Programming style will be emphasized. Intended for Applied Science students only. (Note: Credit is given for only one of Computer Science 101,114, and 151.) CPSC 210 Computer Program Design I - Programming techniques of intermediate sophistication. Information structures and algorithms which operate on them. Students will undertake a programming project. Prerequisite: Computer Science 116 or 118. (Note: All second year Computer Science students must take this course.) CSED 217 Microcomputer Programming I - Uses of microcomputers in education; introduction to programming in BASIC.  Methodology  Page 43  A l l of the specified courses were given during both terms. Note therefore, that a student could possibly have taken more than one course of computer science during the university year. For students who completed more than one questionnaire, only the first response was recorded.  Questionnaire The students involved in the study completed a one-page questionnaire in order to provide the data required to answer the questions involved in this study. Figure 2 shows the questionnaire used:  Page 44  Methodology Figure 2 The Questionnaire cs  Sec  LEARNING COMPUTER LANGUAGES  T h i s q u e s t i o n n a i r e i s part of a study to analyze the trends i n programming languages. I f you choose to p a r t i c i p a t e your mark i n t h i s course w i l l be obtained from the r e g i s t r a r as part of t h i s study. Responses w i l l be h e l d confidential. Tour i n s t r u c t o r w i l l see a summary of the r e s u l t s o b t a i n e d but w i l l not be a b l e to i d e n t i f y i n d i v i d u a l s t u d e n t s . If you do not wish t o p a r t i c i p a t e OBC Student  i n t h i s study, check Year (1st,  Number  2nd,  t h i s bcx  ...)  £1  Age  Majors_  faculty  If you have never l e a r n e d a computer language, check t h i s box and hand i n your q u e s t i o n n a i r e . You are f i n i s h e d . Three s p e c i f i c languages a r e l i s t e d below p l u s the c a t e g o r y , "Other", to cover languages other than the three s p e c i f i e d . Please c i r c l e , i n column ( l ) , any of the languages you have l e a r n e d and p r o v i d e the f o l l o w i n g i n f o r m a t i o n f o r each language l e a r n e d : In column ( 2 ) : Has t h i s language the 1st language you learned? the 2nd? In column ( 3 ) : How w e l l do you know t h i s language now: 1 2 3 4 5 .-  vaguely f a m i l i a r capable of reading a b l e to w r i t e simple programs (<60 l i n e s ) quite familiar a b l e to w r i t e complex programs <>200 l i n e s )  In column ( 4 ) : At what p o i n t ( s ) i n your l i f e d i d you l e a r n In column ( S ) : Where d i d you l e a r n t h i s language?  —rn— Computer language learned  BASIC  Pascal  —m—  Order of learning 1st [ ] 2nd [] 3rd [ ] laterU  1st t l 2nd [] 3rd t l latert]  ——m  TTi  How w e l l do you know? (check one) 1 [] 2 [] 3 [] 4  []  5 []  n  1 2 ti 3 [] 4  []  5 []  (check one or more) Age up to 13 [] (Elementary school) Age 13 to 13 [] (Secondary school)  LOGO  Other (specify)  n  2nd [] 3rd [] latert]  1st [] 2nd [] 3rd [] latert 1  (]  Age 13 to 18 U (Secondary school)  Age up to 13 [ ] (Elementary school) Age 13 t o 18 [ ] (Secondary school)  n  Age  n  School, Univ. [] Home [j Job tl Other []  S c h o o l . Univ. [] Home [ ] Job [ ] Other t]  18 or more  Age up t o 1 3 [ ] (Elementary school) Age 1 3 to l 3 [] (Secondary 3 c h o o l ) Age  School, Univ. [ ] Home L ] Job [ ] Other []  18 or more'  []  [] [] [] []  Where d i d you learn? (check one or more)  [I  Age up to 13 N (Elementary school)  Age  ist  18 or more  language?  (5)  When d i d you l e a r n ?  Age  this  13 or more  School, Univ Home Job Other  [ ] [] [] [ ]  tl Thank you f o r your  time.  Methodology  Page 45  Permission to distribute the questionnaire in class was requested, and it was granted by each of the ten instructors involved. This was followed by a letter (see Appendix A) with a copy of the questionnaire to be used. The first item on the questionnaire was used to obtain the consent of each student to be involved in the study. For those students who did not wish to be involved in the study nothing else was required to be written on the questionnaire. These students could then return the questionnaire to the writer. Otherwise, students were to write their university student number (in order to facilitate the matching of the questionnaire with the final mark), their year enrolled at university, their age, their sex, their Faculty and their major(s). This information was requested so as to make comparisons among different years, ages, Faculties and majors and between sexes. Those students indicating they had never learned a computer language were finished with the questionnaire. These could then be returned to the writer. Those students who had learned (a) computer language(s) were to indicate if the language^) learned was (were) BASIC, Pascal, LOGO, or "Other", and to respond to the following questions about each language specified. 1) Students were to indicate the numerical order in which the listed languages had been learned - first, second, third, or later; this was included because the writer thought that there might possibly be some achievement difference between students who learned BASIC as their first language and those who learned BASIC other than as the first language. The data concerning the order in which the computer languages were learned were gathered because of the assertions of Bork and Dijkstra that learning BASIC first causes students to acquire bad programming habits which cannot be overcome. 2) The students were to check how well they knew each of these languages on a five-point scale from being (1) "vaguely familiar" to being (5) "able to write complex programs" of more than 200 lines in length. Such data were requested because the achievement might  Methodology  Page 46  possibly have been affected by how well previous languages had been learned. The criteria for the five-point scale was decided upon because of the distinct ability groupings. 3) Respondents were to answer how old they were when each language was learned. Three age ranges were given: age up to 13 (elementary school), age 13 to 18 (secondary school), and age 18 or more. They could make more than one selection for each language. The data were needed because the achievement might possibly have been affected by how long ago previous languages had been learned. These age ranges were used since it was felt to be the easiest classification for the respondent. 4) Students were asked where each language had been learned. Four selections were given for choices - at school or university, at home, on a job, or "other". If students chose "other" they were to indicate where the language had been learned. The "location" data were needed since there may have been an achievement difference due to the different environments in which the previous languages were learned. Pilot  Testing  Before the study began the questionnaire was tested on nine Grade 12 secondary school students enrolled in a Computer Science 12 course to see if the instructions on the questionnaire were clear and unambiguous, and to learn how much time was required to complete the questionnaire. The pilot sample was chosen since it was readily available at the school where the writer taught. One disadvantage of the pilot test was that for the pilot students some of the items were inappropriate e.g. Faculty, majors. However, every student had learned BASIC language. The writer asked the students to specify which items (if any) were unclear to them; there were none. The time required by the students to complete the questionnaire was carefully recorded. The questionnaires were collected and reviewed by the writer. No changes were made in the questionnaire after the pilot testing.  Methodology  Page 47  Course Grades At the conclusion of Term 2 a computer file was set up containing information for each student who had been enrolled in the courses of this study. Two important fields of information in the file were the final computer science course grade converted to a percentage (to permit easier comparisons and interpretations of results), and the average (in percentage) for all courses completed during the two terms of the study. (The course mark in computer science was used as a measure of computer science achievement.) Other fields contained the number of courses in which the student was enrolled, the number of computer courses studied, the standing for the student in all courses taken and the current status of the student within the university. The course grades and year average (in percentage) were identified by student number so that they could be matched with the responses to the questionnaire. Individual student grades were kept in the strictest confidence. The responses from the questionnaire sorted by student number were coded and entered into a file on the U.B.C. Amdahl computer. The file containing the grades was then merged with the questionnaire file. Student numbers with information missing were excluded from the study. The coding scheme is included in Appendix B.  Questions The results of questionnaires returned were collected as data in order to help answer the following questions.  BASIC Language  Backgrounds  Five questions pertain to comparisons among groups with various BASIC backgrounds.  Methodology  1  Page 48  How does introductory computer science achievement compare between the group of students whose first computer language was BASIC and the group who knew no languages?  2  Does achievement differ between the group with prior knowledge of BASIC and the group who had prior knowledge of computer languages other than BASIC?  3  Does achievement differ between the group with prior knowledge of BASIC only and the group who had prior knowledge of BASIC and another language?  4  Of all students with prior knowledge of BASIC does achievement differ between the group who had learned BASIC first and the group who learned another language before learning BASIC?  5  Are there differences in achievement among the groups of students who have already learned 0, 1, 2, 3, or 4 computer languages?  Demographic  Backgrounds  Four questions deal with comparisons among groups with different backgrounds, other than computer languages learned. 6 Is there a difference in achievement by gender? 7  Are there differences in achievement among various age groups?  8 Are there differences in achievement among the Faculties represented? 9  Is there a difference in achievement between students who are majoring in mathematics and students who are majoring in various fields within the Faculty of Arts (i.e. non-mathematics majors)?  Different Language Backgrounds Two questions are concerned with comparisons among groups enrolled in an introductory computer science course with different computer language backgrounds, and among similar groups in a second year computer science course.  Methodology  10  Page 49  Is there a difference in achievement among students with different computer language backgrounds enrolled in thefirstyear FORTRAN (CPSC 101 or CPSC 151) or Pascal (CPSC 114 or CPSC 118) courses?  11  Is there a difference in achievement among students with different computer language backgrounds enrolled in the second year course (CPSC 210)?  BASIC and Pascal Backgrounds Three questions pertain to comparisons among groups with different BASIC language backgrounds, and among groups with different Pascal language backgrounds. 12  Are there differences in achievement among the groups that respond in five different ways regarding "how well" a previous language (either BASIC or Pascal) was learned?  13  Are there differences in achievement among the groups that respond in three different ways regarding "when" either BASIC or Pascal was learned?  14  Are there differences in achievement among the groups that respond in four different ways regarding "where" either BASIC or Pascal was learned?  Achievement Predictors One question deals with the relationship between computer science achievement and a set of predictor variables. 15  Which factor(s) under investigation is/are the best predictor(s) of success in university introductory courses in computer science?  Methods of Analysis In order to test the hypotheses used to answer thefirstfourteen questions stated in the study an analysis of covariance was used. The effect of certain variables on the computer science course mark (MARK) was investigated while the influence of the percentage obtained in all other courses (PERCENT) was statistically controlled. PERCENT was cal-  Methodology  Page 50  culated by removing the computer science course mark from the year average for all courses. The data were analyzed using the multivariate analysis of variance and covariance procedure (MANOVA) contained in the Statistical Package for the Social Sciences (SPSS). With a single dependent variable and covariate MANOVA was chosen instead of the analysis of covariance procedure (ANCOVA) because the interrelations among the independent variables were desired. The SPSS ANCOVA routine does not provide such post-hoc comparisons. Group means were adjusted for the covariate (PERCENT). Both the "Actual Mean" and the "Adjusted Mean" are reported for each test. If the independent variable tested had more than two levels then a square matrix of contrast coefficients for this factor was entered in order to observe the between-group comparisons. The matrix contained as many rows and columns as there were levels of the factor. The coefficients entered in a row indicated the desired comparison between the groups of the factor. For example, the contrast coefficients 1 0 - 1 mean that the effect of group 1 is to be compared with the effect of group 3 on the dependent variable. The contrast coefficients 1/2 1/2 -1/3 -1/3 -1/3 mean that the effect of the average of groups 1 and 2 is to be compared with the effect of the average of groups 3,4 and 5. Each set of contrast coefficients summed to zero, and each row was not a linear combination of any other rows. Only those between-group comparisons thought to be of interest for this study in the view of the writer were entered. On occasion in testing some hypotheses more than one matrix of coefficients was entered. Also, on some occasions superfluous comparisons were entered in order to build the required square matrix. These extra comparisons were not reported. For each MANOVA an omnibus F ratio was obtained. If this value was not significant, then the results of the between-group comparisons were not significant. If the omnibus F was significant, then the F ratio given for each complex contrast was compared with a critical Ftable value (Kirk, 1968). A table containing the above information for each statistical test required to answer questions 1 to 14 is provided in Chapter Four.  Methodology  Page 51  To answer Question 15 a multiple linear regression equation was formed using the various factors as predictor scores and the computer science course grade as the criterion score using the SPSS subprogram REGRESSION. The best linear prediction equation and its prediction accuracy for calculating MARK were desired. To this end, a stepwise selection method of multiple linear regression was used to analyze the relationship between a set of 10 predictors and a single criterion variable. A significance level of 0.05 was chosen. Post-hoc comparisons were made using Scheffe's procedure (Kirk, 1968) with a significance level of 0.10. Ferguson (1981) and Scheffe (1959) both recommend a more liberal level of significance. Although the omnibus F significance level was set at 0.05 an experiment error rate greater than 0.05 existed since so many statistical tests were performed.  £age 52  Chapter Four  Results Descriptive Analyses  Two types of data were collected in this study. First was the responses to the questionnaire described in Chapter Three. Second was the final course mark obtained by 1602 students enrolled in introductory computer science courses at the University of British Columbia during either the first or second term of the 1985-86 winter session. These marks were obtained via magnetic tape from the Office of the Registrar. The questionnaire was answered by 1302 students enrolled in introductory computer science courses. Responses to the questionnaire were not received from 365 students who had received a course mark. Of these missing responses: -44 students indicated that they did not wish to be involved in this study; -48 students had incomplete or missing student numbers recorded on the questionnaire; -273 students did not return the questionnaire, were absent at thetimeof the questionnaire distribution, or were not yet enrolled in the computer science course at the time the questionnaire was administered. The students enumerated above were not included in the study since no background information was known about them. On the other hand, 55 students had completed a questionnaire but, for various reasons, no course mark was assigned for them: 21 of the 55 were females, 22 had learned BASIC language, and between 3 to 13 students inclusive, were from each of the six courses in the study. These were removed from the study once it was determined that no common characteristics could be found among them. Eight students completed a questionnaire in both first and second term. Only the first completed questionnaire was used because the study focuses on the first computer science course taken at the university. In addition, two students were enrolled in two first term computer courses; both completed two questionnaires. One of these students received marks in both  Page 53  Results  courses and was excluded from the study. The other student had a mark for only one of the two courses and was retained in the study. The two data types, questionnaire data and marks, were merged by matching student numbers resulting in 1236 records. Of the 1236 records, 42 students had a final mark recorded as 0 given in the computer science course. This indicates that they had unofficially dropped out of the course. These were excluded and a final sample of 1194 remained. In the sample the number of students enrolled in each of the surveyed courses is shown in the Table 3: Table 3 Distribution of Students in Computer Courses Course Number CPSC 101 CPSC 151 CPSC 114 CPSC 118 CPSC 210 CSED 217 Total  (FORTRAN) (FORTRAN) (Pascal) (Pascal) (Pascal) (BASIC)  Number of Students 347 245 338 116 95 53 1194  Percent of Sample 29.1 20.5 28.3 9.7 8.0 4.4 100.0  There were approximately equal numbers of students in the FORTRAN courses numbered 101 and 151 (592 students, 49.6% of the sample) compared to the Pascal courses numbered 114, 118, and 210 (549 students, 46% of the sample). Table 4 shows the years in which the students were enrolled. The majority were in first and second year (82.7%).  Results  Page 54  Table 4 Distribution of Students by Year of Registration Year  Number of Students  1st 2nd 3rd 4th 5th 6th 7th 8th 10th Not stated Total a  Percent of Sample  555 432 104 62 28 4 3 1 1 4 1194  46.5 36.2 8.7 5.2 2.4 0.3 0.3 0.1 0.1 0.3 100.1"  Column totals more than 100.0 percent due to rounding.  The median age of the respondents was 19 years. The mean age was 20.2 years. There were 329 female students (27.6%) and 859 males (71.9%) and six missing values (0.5%). The students were distributed across a number of Faculties. Table 5 shows the Faculties in which the students were enrolled. Table 5 Distribution of Students by Faculty Faculty Science Applied Science Commerce & Business Administration Arts Education Agricultural Sciences Forestry Graduate Studies Unclassified Not stated Total a  Column totals more than 100.0 percent due to rounding.  Number of Students 365 322 199 196 59 21 5 3 20 4 1194  Percent of Sample 30.6 27.0 16.7 16.4 5.0 1.8 0.4 0.3 1.7 0.3 100.2*  Page 55  Results  The Faculties of Science, Applied Science, Arts, and Commerce and Business Administration accounted for more than 90% of the sample. Figure 3 shows the percentage of students in the sample who had learned one or more computer language prior to entering this course. Figure 3  Distribution of Students with Prior Knowledge of a Computer Language  •  •  No Languages One or more Languages  Figure 4 shows the percentage of students in the sample who had learned some BASIC language prior to entering this course.  Results  Page 56  Figure 4 Distribution of Students with Prior Knowledge of BASIC Computer Language  •  No BASIC  H  Knew BASIC  Of the 757 students who had previously learned some BASIC language, 698 students had learned BASIC as their first computer language (92.2% of those knowing BASIC). For these students the order of their learning BASIC language is summarized in Table 6, below. Table 6 Order of Learning BASIC for Students with Prior Knowledge of BASIC Language Order of learning BASIC  Number of Students  Percent of Total  First Language Second Language Third Language Fourth Language Not stated Total  698 40 12 4 3 757  92.2 5.2 1.6 0.5 0.4 99.9  Column totals less than 100.0 percent due to rounding.  a  Results  Page 57  Most of the students who had learned BASIC indicated that they knew at least enough BASIC in order to "write simple programs" up to 60 lines (596 out of 757 students 78.7%) and that they learned BASIC in school (628 out of 757 students - 83.0%) and were between the ages of 13 and 18 when they first learned BASIC (625 out of 757 students 82.6%). BASIC was learned at home by 201 students (26.6% of 757 students). Of these, 105 had not learned any BASIC at school. The students' experiences with BASIC are summarized in Tables 1 - 4 of Appendix C. Figure 5 shows the percentage of students in the sample who had learned some Pascal language prior to entering this course. Figure 5 Distribution of Students with Prior Knowledge of Pascal Computer Language  H  Knew Pascal  H  No Pascal  Of the 339 students who had previously learned some Pascal language, 178 students had learned Pascal as their second computer language (52.5% of those knowing Pascal). For these students the order of their learning Pascal language is summarized in Table 7, below.  Page 58  Results  Table 7 Order of Learning Pascal for Students with Prior Knowledge of Pascal Language. Order of learning Pascal First language Second language Third language Fourth language Not stated Total  Number of Students 54 178 85 19 3 339  Percent of Total 15.9 52.5 25.1 5.6 0.9 100.0  Of the students who had learned Pascal, 103 out of 339 students (30.4%), indicated that they were able to "write complex programs" of more than 200 lines - the highest classification possible for the response to "How well do you know?" on the questionnaire. Of the "Pascal-knowledgeable" students, 188 out of 339 students (55.5%), had learned Pascal at age 18 or more and 309 out of 339 students (91.2%) at school/university . No student had learned Pascal before age 13. The students' experiences with Pascal are summarized in Tables 5 - 8 of Appendix C. Only 71 students (6% of the sample) had learned the LOGO language. Nine of these students had learned LOGO as their first language (12.7% of 71 students). The majority of LOGO learners indicated that they were not "quite familiar" with LOGO, nor "able to write complex programs" (63 out of 71 students - 87.7%). LOGO had been learned between ages 13 and 18 by 61 out of 71 students (85.9%) and 42 "LOGO-knowledgeable" students (59.1% of 71 students) had learned LOGO at school. The students' experiences with LOGO are summarized in Tables 9 - 12 of Appendix C. Figure 6 shows the number of students in the sample who knew a language and which language was learned first.  Results  Page 59  Figure 6 First Learned Computer Language  Other 63  Figure 7 shows a Venn diagram of the number of students in the sample who had learned either BASIC, Pascal or LOGO languages or any combinations of these three. Of the 805 students who had previously learned one of these languages, 57 knew all three languages, and no student knew just LOGO language.  Results  Page 60  Figure 7 BASIC, Pascal, LOGO Languages Learned  A language other than BASIC, Pascal, or LOGO had previously been learned by 299 students (25% of the sample). The "Other" computer languages known included the following: FORTRAN, Assembler, Machine Language, PL/1, Forth, COBOL, DBase 2, APL, C, Comal and others. The most commonly known "Other" computer language for this group was FORTRAN (170 out of 299 students - 56.9%). This "Other" language had been learned as the first computer language by 63 out of 299 students (21.1%). Most of the "Other" language-knowledgeable students indicated that they knew at least enough of this language in order to "write simple programs" up to 60 lines (222 out of 299 students 74.2%). The majority of these "Other" language-knowledgeable students learned this language at "age 18 or more" (179 out of 299 students - 59.9%), and at school (233 out of 299 students - 77.9%). The students' experiences with "Other" languages are summarized in Tables 13 - 16 of Appendix C.  Results  Page 61  Figure 8 indicates which computer language had been learned first by the 835 students in the sample who had learned a language prior to entering this course. Figure 8 Languages First Learned by 835 Students with Prior Knowledge of a Computer Language  •  BASIC  H  Other Languages  H  LOGO  111 Pascal •  Not stated  Figure 9 shows the order of learning each computer language involved in this study for the 835 students who had previously learned a language. (A few students indicated that they had learned a language but they did not indicate the order of learning that language.)  Results  Page 62  Figure 9 Order of Learning each Computer Language for Students with Prior Knowledge 698  BASIC  Pascal  LOGO  Other  Computer Language Learned  •  1st  H  2nd  IH 3 r d •  4th  From this sample of 1194 students the number of computer science or computer science education courses taken is shown in Table 8.  Table 8 Distribution of Students by Number of Computer Science Courses Taken  a  Current Number of Computer Courses  Number of Students  Percent of Sample  1 2 3 4 5 6 Total  855 278 42 9 7 3 1194  71.6 23.3 3.5 0.8 0.6 0.3 100.1"  Column totals more than 100.0 percent due lo rounding.  Results  Page 63  Of 1194 students only 61 students took more than two computer courses during the year, the equivalent of two terms or two half-year courses. The majority (71.6%) took only the one half-year course. The computer language background of students varied gready between computer science courses. Figure 10 shows the percentage of students in each surveyed course that had previously learned a computer language. Figure 10 Distribution of Students with Prior Knowledge of a Computer Language by Course 100%  100%  100%  75%  Percent of Students with Prior Language  5  Q  %  25%  0% CPSC 101  CPSC 151  CPSC 114  CPSC 118  CPSC 210  CSED 217  Course  All 95 students in CPSC 210 had learned Pascal because the prerequisite for this course is the completion of either CPSC 118 or CPSC 114 and CPSC 116. All 116 students in CPSC 118 had learned a language since prior knowledge of, and experience with programming was assumed as an entrance requirement. Figures 11-14 show the percentages  Results  Page 64  of students in each course that had previously learned BASIC, Pascal, LOGO, and "Other" languages. Figure 11 Distribution of Students with Prior Knowledge of BASIC Language by Course 100% T  85%  75% Percent of Students 50% Knowing 25% B  A  S  I  69%  65% , '  !  '  85%  w  j  *  <. <  hi t i •  plffll Iliill  i M f  C  43%  '  Pitilll '  CPSC 101  1  CPSC 151 CPSC 114 CPSC 118 Course  CPSC 210  CSED 217  Figure 12 Distribution of Students with Prior Knowledge of Pascal Language by Course 100% 100% I"'" -j,  'ii  75% Percent of Students 50% Knowing 25% Pascal 0%  55% 25%  25%  I titlN  & \  25%  6% CPSC 101  CPSC 151 CPSC 114 CPSC 118 Course  CPSC 210  CSED 217  Results  Page 65 Figure 13  Distribution of Students with Prior Knowledge of LOGO Language by Course. 100% 75% • Percent of Students Knowing 1  X  3  0  0  50% 25% 2%  7%  7%  9%  CPSC 151  CPSC 114  CPSC 118  14% 4%  0%  CPSC 101  CPSC 210  CSED 217  Course  Figure 14 Distribution of Students with Prior Knowledge of Other Languages by Course.  CPSC 101  CPSC 151  CPSC 114  CPSC 118  CPSC 210  CSED 217  Course  A computer language had been learned previously by 57.4% of the females (189 students) and 74.6% of the males (641 students). Figure 15 shows the percentages of each gender that had previously learned a computer language.  Results  Page 66  Figure 15 Percentages by Gender with Prior Knowledge of Computer Languages 25.40% 42.60% 57.40% 74.60%  Females  Prior Knowledge  Males  No Prior Knowledge  Thus, males in this study had a "greater" entry level background of computer languages than females. Achievement in Introductory Computer Science Courses The average (mean) mark obtained in the computer science course by 1194 students in the sample was 68.9%. The average mark obtained in all other courses for those who took more than one course was 64.8%. (Only eight students in the sample of 1194 students were enrolled in just one course). A Student t-test for dependent means was conducted of this difference in mean scores. It was found to be significant (t=10.5, p<0.001). See Table 9. That is, students performed significantly better in their computer science course than in their other courses. This difference can be accounted for by many factors - students who worked harder in a course of interest, students who had previous experience in computer science, and others.  Results  Page 67  Table 9  Comparison of Marks in Computer Science Course with Averages in all Other Courses Number of Cases Mark in Surveyed Course 1186  Mean Standard Correlation T 2-Tail Deviation Value Probability 68.9  14.9 0.534  Mark in Other Courses  1186  64.8  10.50  p< 0.001  12.7  Inferential Analyses To answer the first 14 questions stated in Chapter Three analyses of covariance were conducted with computer science course percent (MARK) as the dependent variable, the percentage obtained in all other courses (PERCENT) as the covariate, and many different factors depending upon the question, used as independent variables. An omnibus F significance level of 0.05 was used. Post-hoc comparisons were made at the 0.10 significance level. Many of these questions were analyzed using the entire sample, the introductory FORTRAN (CPSC 101 and CPSC 151) students only, and the introductory Pascal (CPSC 114 and CPSC 118) students only. The second and third analyses were done to compare differences that might occur among students enrolled in introductory FORTRAN courses with the differences among those registered in introductory Pascal courses. Multiple linear regression analysis was used with the fifteenth question.  Page 68  Results  Results on BASIC Language  Backgrounds  The first five questions deal with comparisons of achievement of a group having BASIC language knowledge with that of groups who have learned other languages, and with the order in which the BASIC was learned in relation to other languages. BASIC versus No Language  How does introductory computer science achievement compare between the group of students whose first computer language was BASIC and the group who knew no languages? Hypothesis 1: There is no significant difference in computer science course achievement between the group of students who entered the course with BASIC learned as theirfirstcomputer language and the group who entered without a knowledge of any computer language. A significant difference in favour of the group having had BASIC instruction was found at the 0.05 level between the two groups (F=86.20, p<0.001). See Table 10. In the first year FORTRAN (CPSC 101 and CPSC 151) courses the group of students who had learned BASIC language had a significantly greater achievement than the group who had not yet learned a language (F=58.40, p<0.001). Similarly, in the first year Pascal (CPSC 114 and CPSC 118) courses those who had learned BASIC as their first language had significantly greater achievement than those who had not yet learned a language (F=37.05, p<0.001). Thus the null hypothesis was rejected.  Results  Page 69  Table 10 Comparison of Results of Students Who Learned BASIC as the First Language and Students Who Knew No Languages  Groups:  Learned BASIC first Never learned a language  Total  Number of Students  698  359  1057  Actual Mean  71.0  64.7  68.9  Adjusted Mean  71.5  64.2  F (1,1054) = 86.20, p<0.001 BASIC versus Other Languages  Does achievement differ between the group with prior knowledge of BASIC and the group who had prior knowledge of computer languages other than BASIC? Hypothesis 2: There is no significant difference in achievement between the group who had learned BASIC and the group who had learned languages other than BASIC. A significant difference in favour of the group having had BASIC instruction was observed at the 0.05 level between the two groups (F=4.69, p=0.031). See Table 11. Hypothesis 2 was rejected. However, no significant differences were found between the two groups in either the FORTRAN (CPSC 101 and CPSC 151) courses (F=0.03, p=0.872) or the introductory Pascal (CPSC 114 and CPSC 118) courses (F=2.62, p=0.107).  Results  Page 70  Table 11 Comparison of Results of Students Who Had Learned BASIC and Students Who Had Learned Other Languages Groups:  Number of Students Actual Mean Adjusted Mean  Learned BASIC  Learned languages other than BASIC  Total  757  78  835  70.9 71.5  69.0 68.4  70.7  F (1,832) = 4.69, p=0.031  BASIC Only versus BASIC and Other  Does achievement differ between the group with prior knowledge of BASIC only and the group who had prior knowledge of BASIC and another language? Hypothesis 3: There is no significant difference in achievement between the group who had learned BASIC only and the group who had learned BASIC and another language. A significant difference in favour of the group having learned BASIC and at least one additional language was found at the 0.05 level in course achievement between the two groups (F=8.57, p=0.004). See Table 12. Similarly, in the FORTRAN (CPSC 101 and CPSC 151) courses (F=6.44, p=0.012) and in the Pascal (CPSC 114 and CPSC 118) courses (F=23.57, p<0.001) the group of students who had learned BASIC and another language outperformed those who had learned BASIC language only. Hypothesis 3 was rejected.  Results  Page 71  Table 12 Comparison of Results of Students Who Had Learned Only BASIC and Students Who Had Learned BASIC and Another Language Groups:  Learned BASIC only  Learned BASIC plus other languages  Total  Number of Students  377  380  757  Actual Mean  69.5  72.2  70.9  Adjusted Mean  69.6  72.1  F (1,754) = 8.57, p=0.004 BASIC First versus Not BASIC First  Of all students with prior knowledge of BASIC does achievement differ between the group who had learned BASIC first and the group who learned another language before learning BASIC? Hypothesis 4: There is no significant difference in achievement between the group who learned BASIC as their first computer language and the group who learned another language before learning BASIC. No significant difference was obtained at the 0.05 level between the group of students who had learned BASIC first and the group of students who had learned BASIC other than as their first language (F=0.12, p=0.724). See Table 13. No significant differences were found between the two groups in either the FORTRAN (CPSC 101 and CPSC 151) courses (F=1.10, p=0.295) or the first year Pascal (CPSC 114 and CPSC 118) courses (F=0.99, p=0.32). Hypothesis 4 was not rejected.  Results  Page 72  Table 13 Comparison of Results of Students Who Had Learned BASIC First and Students Who Had Learned BASIC Other than as a First Language Groups:  Learned BASIC first  Learned BASIC but not first  Total  Number of Students  698  56  754  Actual Mean  71.0  69.9  70.9  Adjusted Mean  70.7  70.2  '  F (1,751) = 0.12, p=0.724 Number of Computer Languages (0-4)  Are there differences in achievement among the groups of students who have already learned 0, 1,2, 3, or 4 computer languages? Hypothesis 5: There are no significant differences in achievement among the groups of students who have previously learned 0,1,2, 3, or 4 computer languages. Significant differences were found at the 0.05 level among the groups of students who previously learned 0, 1, 2, 3, or 4 computer languages (F=25.38, p<0.001). Generally, the more languages learned, the higher the course mark. The group of students with no prior language experience had significandy lower achievement than the average of the other four groups with either one, two, three, or four previous languages learned (F=99.88, p<0.10). The average mark of those who had learned more than one language was significantly greater than that of those who did not know a language or had learned just one language (F=58.84, p<0.10). The group of students who had previously learned one language obtained significandy higher achievement than the group without the knowledge of a prior language (F=45.73, p<0.10). See Table 14. Hypothesis 5 was rejected.  Results  Page 73  Table 14 Comparison of Results of Students with Various Number of Computer Languages Learned Groups:  No previous One previous Two Three Four Total language language languages languages languages  Number of Students  360  434  201  165  34  1194  Actual Mean  64.7  69.7  70.6  71.7  78.7  68.9  Adjusted Mean 63.9  69.8  71.6  71.0  79.1  1/4 -1/2 -1 1  1/4 1/3 0 -1  1/4 1/3 0 0  1/4 1/3 0 0  Contrast Coefficients:  -1 -1/2 1 0  F 99.88* 58.84* 45.73* 2.99  omnibus F (4,1 188) = 25.38, p<0.001 * p<0.10, F =7.88 Results on Demographic Backgrounds The next four questions are concerned with differences in achievement among demographic backgrounds: gender, age, Faculty and majors. Gender  Is there a difference in achievement by gender? Hypothesis 6: There is no significant difference in achievement by gender. A significant difference in achievement was found at the 0.05 level between males and females in the courses observed (F=15.51, p<0.001). See Table 15. Males had significandy greater achievement than females. Males had significandy higher computer science course marks at the 0.05 level:  Results  Page 74  -in the FORTRAN (CPSC 101 and CPSC 151) courses (F=17.40, p<0.001); -in the introductory Pascal (CPSC 114 and CPSC 118) courses (F=5.53, p=0.019); -in the group of all five introductory courses (F=14.37, p<0.001). There were no significant gender differences in achievement at the 0.05 level in the second year course (CPSC 210) (F=0.02, p=0.898). Hypothesis 6 was rejected. Table 15 Comparison of Student Results by Gender Groups:  Female  Male  Total  Number of Students  329  859  1188  Actual Mean  68.1  69.1  68.9  Adjusted Mean  67.0  70.2  F (1,1185) = 15.51, p<0.001 Age Are there differences in achievement among various age groups? Hypothesis 7: There are no significant differences in achievement among age groups. Students were divided into three groupings by age: ages 17-19, ages 20-24, and ages 25 and over, from the ages as recorded on the questionnaires. Significant achievement differences were found at the 0.05 level among the age groups (F=6.40, p=0.002). The youngest group had significantly greater achievement than the oldest group (F=10.61, p<0.10), and significantly greater achievement than the average of the other two groups combined (F=12.76, p<0.10). See Table 16. Hypothesis 7 was rejected.  Results  Page 75  Table 16 Comparison of Student Results by Age 17-19  20-24  Number of Students  708  378  99  1185  Actual Mean  69.6  68.1  66.2  68.9  Adjusted Mean  70.0  68.3  65.7  0 -1 1/2 1  1 0 1/2 -1  Age Groups  Contrast Coefficients:  -1 1 -1 0  25 and over  Total  F 10.61* 4.50 12.76* 3.61  omnibus F (2,1181) = 6.40, p=0.002 * p<0.10, F' = 4.66 Faculty  Are there differences in achievement among the Faculties represented? Hypothesis 8: There are no significant differences in achievement among the Faculties represented. Significant MARK differences were observed at the 0.05 level among the Faculties represented (F=15.48, p<0.001). Significant differences in achievement were observed between the Faculty of Applied Science and the average of the other faculties represented (F=37.60, p<0.10). The Faculty of Applied Science students also had greater achievement than each of: the Faculty of Arts students (F=62.14, p<0.10), the Faculty of Education students (F=47.19, p<0.10), the Faculty of Science students (F=21.50, p<0.10). Excluding the Faculty of Applied Science significant differences in achievement were found between the Faculty of Commerce and Business Administration and the average of the other faculties represented (F=14.03, p<0.10). The Faculty of Commerce and Business  Results  Page 76  Adrninistration students also had higher marks than the Faculty of Arts students (F=27.67, p<0.10) and the Faculty of Education students (F=28.50, p<0.10). Students in the Faculty of Science had significantly higher achievement than students in the Faculty of Arts (F=16.36, p<0.10). See Table 17. Hypothesis 8 was rejected. Table 17 Comparison of Student Results by Faculty Faculties  Number of Students  Science Arts Commerce & Applied Business Science Administration  Forestry Eduand cation Agriculture  Graduate Total Studies/ Unclassified  365  196  199  322  26  59  23  1190  Actual Mean  66.4  63.0  73.0  73.4  71.4  62.8  71.8  68.9  Adjusted Mean  69.5  65.2  71.7  73.9  70.3  62.1  69.3  1/6 1/5 -1 0 0 1 -1 0 0  1/6 -1 0 1 -1 0 1 0 0  -1 0 0 0 0 -1 0 -1 -1  1/6 1/5 0 0 0 0 0 0 0  1/6 1/5 0 0 1 0 0 1 0  1/6 1/5 0 0 0 0 0 0 0  Contrast Coefficients: 1/6 1/5 1 -1 0 0 0 0 1  F 37.60* 14.03* 16.36* 3.84 28.50* 62.14* 27.67* 47.19* 21.50*  omnibus F (6,1182) = 15.48, p<0.001 * p<0.10, F' = 10.8 Major Is there a difference in achievement between students who are majoring in mathematics (enrolled in either the Faculty of Arts or the Faculty of Science) and students who are majoring in various fields within the Faculty of Arts (i.e. non-mathematics majors)?  Results  Page 77  Hypothesis 9: There is no significant difference in achievement between the group of stu dents who are majoring in mathematics and the group who are majoring in various fields within the Faculty of Arts. The group of students majoring in mathematics had significandy greater achievement than the group majoring in various Departments and Schools within the Faculty of Arts (F=6.19, p=0.015). See Table 18. A complete listing of the majors indicated by these Faculty of Arts students with the number for each major, is contained in Table 19. In the introductory Pascal (CPSC 114 and CPSC 118) courses the mathematics majors significantly outperformed the non-mathematics majors (F=9.59, p=0.003). In the FORTRAN (CPSC 101 and CPSC 151) courses no statistical testing was done because of the imbalance in the number of students who were mathematics majors (n=l) and non-mathematics majors (n=29). A reason for this imbalance is that the CPSC 114 course or CPSC 118 course is a requirement for a Bachelor of Science degree as a mathematics major. Hypothesis 9 was rejected. Table 18 Comparison of Student Results for Selected Majors Groups:  Math majors  Non-math majors  Total  Number of Students  48  53  101  Actual Mean  66.6  61.5  63.9  Adjusted Mean  67.1  61.0  F (1,98) = 6.19, p=0.015  Results  Page 78  Table 19 Number of Non-mathematics Majors in the Faculty of Arts Major Asian Studies Dietetics Economics English International Relations Languages, Linguistics Law Music Political Science Psychology Sociology Total  Number of Students 1 2 26 1 1 7 1 1 1 11 1 53  Results on Different Language Backgrounds The next two questions deal with achievement differences among students with different computer language backgrounds enrolled in an introductory FORTRAN or Pascal course, and achievement differences among students with different computer language backgrounds enrolled in a second year computer science course. It might be speculated that previous programming experience might have an effect on achievement in a course taught in one programming language, but have little effect on achievement in another course employing a different computer language. Different Backgrounds in First Year  Is there any difference in achievement among students with different computer language backgrounds enrolled in thefirstyear (a) FORTRAN (CPSC 101 or CPSC 151) or (b) Pascal (CPSC 114 or CPSC 118) courses?  Hypothesis 10(a): There is no significant difference in achievement among students with different computer language backgrounds enrolled in an introductory FORTRAN course.  Results  Page 79  In order to investigate this question, students were grouped according to their previous experience with computer languages. The groups were composed of students who had learned: 1. BASIC language only; 2. BASIC language first, and at least one other language; 3. BASIC subsequent to learning at least one other language; 4. at least one language, but not BASIC language; 5. no computer languages yet. A significant achievement difference was realized at the 0.05 level among these five groups of students with different computer language backgrounds (F=17.08, p<0.001). See Table 20. Students who knew no computer languages prior to the course (Group 5) had significantly lower achievement than the average of those who had some computer language background (F=37.76, p<0.10). Students who had no prior language experience (Group 5) achieved significantly less than those who knew BASIC only (Group 1) (F=35.63, p<0.10), and significantly less than those who had learned BASIC as their first language as well as at least one more language (Group 2) (F=51.93, p<0.10). No significant difference was found between students who had learned BASIC as their first language (Groups 1&2) and those who had learned a language other than BASIC as their first language (Groups 3&4) (F=0.00). Hypothesis 10(a) was rejected.  Results  Page 80  Table 20 Comparison of Results Based on Computer Language Background in the FORTRAN (CPSC 101 and CPSC 151) Courses Groups  (1) BASIC (2) BASIC first (3) BASIC (4) At least 1 (5) No Total only and at least after at least computer language 1 other 1 other but not languages language language BASIC  Number of Students  235  87  10  25  234  591  Actual Mean  73.5  75.6  74.5  74.8  67.1  71.3  Adjusted Mean  72.8  76.8  75.3  74.1  66.5  1/4 -1 1/3 0 1/2 0  1/4 0 1/3 1 1/2 0  1/4 0 1/3 0 -1/2 -1  1/4 0 -1 0 -1/2 0  Contrast Coefficients:  -1 1 0 -1 0 1  F 37.76* 35.63* 0.10 51.93* 0.00 5.83  omnibus F (4,585) = 17.08, p<0.001 * p<0.10, F' = 7.88  Hypothesis 10(b): There is no significant difference in achievement among students with different computer language backgrounds enrolled in an introductory Pascal course. A significant achievement difference was realized at the 0.05 level among the same five groups of students with different computer language backgrounds (F=15.53, p<0.001). See Table 21. Students who knew no computer languages prior to the course (Group 5) had significandy lower achievement: -than the average of those who had some computer language background (F=28.32, p<0.10); -than those who knew BASIC only (Group 1) (F=8.92, p<0.10); -than those who had learned BASIC as the first language and at least one other language (Group 2) (F=54.76, p<0.10);  Results  Page 81  -than those who had learned BASIC subsequent to learning at least one other language (Group 3) (F=15.53, p<0.10). Also, the group of students who had learned BASIC language first, and at least one other language (Group 2) had significantly higher achievement than the group of students who had learned BASIC language only (Group 1) (F=20.42, p<0.10). Again, no significant difference was found between students who had learned BASIC as their first language (Groups 1&2) and those who had learned a language other than BASIC as their first language (Groups 3&4) (F=0.03). Hypothesis 10(b) was rejected. Table 21 Comparison of Results Based on Computer Language Background in the Pascal (CPSC 114 and CPSC 118) Courses Groups  (1) BASIC (2) BASIC first (3) BASIC (4) only and at least after at least 1 other 1 other language language  Number of Students  At least 1 (5) No Total language computer but not languages BASIC  130  169  19  29  104  451  Actual Mean  62.5  70.3  72.1  66.2  59.2  65.3  Adjusted Mean  64.3  70.7  71.4  64.3  59.6  1/4 -1 1/3 0 1/2 0 -1  1/4 0 1/3 1 1/2 0 1  1/4 0 1/3 0 -1/2 -1 0  1/4 0 -1 0 -1/2 0 0  Contrast Coefficients:  omnibus F (4,445) = 15.53, p<0.001 * p<0.10, F = 7.88  -1 1 0 -1 0 1 0  F 28.32* 8.92* 3.44 54.76* 0.03 15.53* 20.42*  Results  Page 82  Different Backgrounds In Second Year Is there any difference in achievement among students with different computer language backgrounds enrolled in the second year course (CPSC 210)? Hypothesis 11: There is no significant difference in achievement among students with different computer language backgrounds enrolled in the second year course (CPSC 210). In order to investigate this question, students were grouped according to their previous experience with computer languages. The groups were composed of students who had learned: 1. BASIC language first, and at least one other language; 2. BASIC subsequent to learning at least one other language; 3. at least one language, but not BASIC language. No significant differences in achievement were found at the 0.05 level among these three student groupings of different computer language backgrounds (F=2.39, p=0.097). See Table 22. Hypothesis 11 was not rejected.  Results  Page 83  Table 22 Comparison of Results Based on Computer Language Background in the Second Year (CPSC 210) Course (1) BASIC first and at least one other language  (2) BASIC after at least one other language  Number of Students  67  14  14  95  Actual Mean  73.4  67.2  60.5  70.6  Adjusted Mean  70.2  67.6  63.3  -1 1/2 1 0 -1  0 1/2 -1 1 1/2  1 -1 0 -1 1/2  Groups  Contrast Coefficients:  (3) At least one language but not BASIC  Total  F 4.60 2.84 0.69 1.09 3.75  omnibus F (2,91) = = 2.39, p=0.097 * p<0.10, F' = 4.78 Results of BASIC and Pascal Backgrounds The following three questions are concerned with the achievement of students with specific background information relating to both BASIC and Pascal languages. "How Well" Language w a s Learned  Are there any differences in achievement among the groups that respond in five different ways regarding "how well" a previous language [either (a) BASIC or (b) Pascal] was learned?  Hypothesis 12(a): There are no significant differences in achievement among the groups that respond in five different ways regarding "how well" BASIC was learned. Students had responded to the question "How well do you know (BASIC, Pascal, etc.)?", by selecting one of the following five choices for each language previously learned:  Results  Page 84  1. vaguely familiar 2. capable of reading 3. able to write simple programs (<60 lines) 4. quite familiar 5. able to write complex programs (>200 lines) Different results were found for BASIC and Pascal languages. For those students with prior knowledge of BASIC significant differences in computer science achievement were found among the five groups at the 0.05 level (F=8.27, p<0.001). See Table 23. Those "able to write complex programs" (Group 5) had significantly greater achievement than the average of those who were not able (F=29.43, p<0.10). The group of students who had entered this course with the ability "to write complex programs" (Group 5) in BASIC had significandy higher achievement than the group who upon entering were either "vaguely familiar" (Group 1) with BASIC language (F=21.84, p<0.10), "capable of reading" (Group 2) BASIC programs (F=14.63, p<0.10), or "able to write simple programs" (Group 3) in BASIC language (F=15.87, p<0.10). Similarly, in the FORTRAN (CPSC 101 and CPSC 151) courses (F=6.77, p<0.001) and in the Pascal (CPSC 114 and CPSC 118) courses (F=8.44, p<0.001) students who were "able to write complex programs" achieved significandy higher than those who were not "able to write complex programs". Hypothesis 12(a) was rejected.  Results  Page 85  Table 23 Comparison of Results Based on "How Well" BASIC Language had been Previously Learned Groups (1) Vaguely (2) Capable (3) Able to write (4) Quite (5) Able to write familiar of simple familiar complex Total reading programs programs Number of Students  80  71  202  157  237  747  Actual Mean  66.4  67.6  69.7  70.9  74.2  70.8  Adjusted Mean  66.8  67.8  69.4  70.9  73.9  1/4 -1 1/3 0 -1 0  1/4 0 1/3 1 0 0  1/4 0 1/3 0 0 -1  Contrast Coefficients:  1/4 0 -1 0 1 0  -1 1 0 -1 0 1  F 29.43* 21.84* 5.96 14.63* 6.34 15.87*  omnibus F (4,741) = 8.27, p<0.001 * p<0.10, F' = 7.88  Hypothesis 12(b): There are no significant differences in achievement among the groups that respond infivedifferent ways regarding "how well" Pascal was learned. For those students with prior knowledge of Pascal no significant differences were found at the 0.05 level among the same five groups on "how well" Pascal language had previously been learned (F=1.72, p=0.145). See Table 24. Similarly, in the FORTRAN (CPSC 101 and CPSC 151) courses (F=1.12, p=0.356) and in the Pascal (CPSC 114 and CPSC 118) courses (F=1.63, p=0.169) no significant differences were found at the 0.05 level among the five groups. Hypothesis 12(b) was not rejected.  Results  Page 86  Table 24 Comparison of Results Based on "How Well" Pascal Language had been Previously Learned Groups (1) Vaguely (2) Capable (3) Able to write (4) Quite (5) Able to write familiar of simple familial complex Total reading programs programs Number of Students  59  34  72  54  103  322  Actual Mean  69.0  70.8  70.6  71.4  74.8  71.8  Adjusted Mean  68.9  71.8  69.9  72.5  73.5  1/4 -1 1/3 0 -1 0 -1 0  1/4 0 1/3 1 0 0 0 0  1/4 0 1/3 0 0 -1 1 0  Contrast Coefficients:  1/4 0 -1 0 1 0 0 -1  -1 1 0 -1 0 1 0 1  F 3.25 5.22 1.37 0.45 2.44 3.53 0.23 0.22  omnibus F (4,316) = 1.72, p=0.145 * p<0.10, F = 7.88 "When" Language was Learned Are there any differences in achievement among the groups that respond in three different ways regarding "when" either (a) BASIC or (b) Pascal was learned?  Hypothesis 13(a): There are no significant differences in achievement among the groups that respond in three different ways regarding "when" BASIC was learned. Students had responded to the question, "When did you learn (BASIC, Pascal, etc.)?", by indicating one or more of the following three choices for each language previously learned: 1. Age 18 or more 2. Age 13 to 18 (Secondary school) 3. Age up to 13 (Elementary school) For students who selected more than one choice the youngest age group selected was used.  Results  Page 87  For those students with prior knowledge of BASIC significant differences in achievement were found at the 0.05 level among the various groups (F=3.16, p=0.043). See Table 25. Students who first learned BASIC at age 13 to 18 had significandy greater achievement than those who first learned BASIC at age 18 or more (F=6.31, p<0.10). Hypothesis 13(a) was rejected. Table 25 Comparison of Results Based on "When" BASIC Language had been Previously Learned Age Groups:  Age 18 or More  Age 13 to 18  Age up to 13  Total  Number of Students  103  625  25  753  Actual Mean  67.8  71.4  70.7  70.8  Adjusted Mean  68.0  71.2  70.6  -1 1 -1 1/2  0 -1 1/2 1/2  1 0 1/2 -1  Contrast Coefficients:  F 0.95 6.31* 2.91 0.16  omnibus F (2,749) = 3.16, p=0.043 * p<0.10, F' = 4.66  Hypothesis 13(b): There are no significant differences in achievement among the groups that respond in three different ways regarding "when" Pascal was learned. For those students with prior knowledge of Pascal significant differences at the 0.05 level were found among the age groups for "when" Pascal was previously learned (F=6.88, p=0.009). See Table 26. Students who first learned Pascal at age 13 to 18 had significantly greater achievement than those who first learned Pascal at age 18 or more. No students in this study had previously learned Pascal prior to age 13. Hypothesis 13(b) was rejected.  Results  Page 88  Table 26 Comparison of Results Based on "When" Pascal Language had been Previously Learned Age Group  Age 18 or More  Age 13 to 18  Total  Number of Students  188  148  336  Actual Mean  70.6  73.5  71.9  Adjusted Mean  70.3  73.8  F (1,333) = 6.88, p=0.009 "Where" Language was Learned  Are there any differences in achievement among the groups that respond in four different ways regarding "where" either (a) BASIC or (b) Pascal was learned?  Hypothesis 14(a): There are no significant differences in achievement among the groups that respond in four different ways regarding "where" BASIC was learned. Students had responded to the question "Where did you learn (BASIC, Pascal, etc.)?" by selecting one or more of the following four choices for each language previously learned: 1. School, university 2. Home 3. Job 4. Other Similar results were found for both BASIC and Pascal languages. For those students with a prior BASIC language background no significant difference in achievement was observed among the various groups at the 0.05 level among the "places" where various groups of students had learned BASIC (F=1.70, p=0.147). See Table 27. Hypothesis 14(a) was not rejected.  Page 89  Results  Table 27 Comparison of Results Based on "Where" BASIC Language had been Previously Learned Job  Home  School  School and Home  Other  Total  Number of Students  4  104  529  91  4  732  Actual Mean  74.7  72.9  70.4  72.5  69.3  71.0  Adjusted Mean  69.3  74.0  71.6  74.2  70.7  1/4 0 -1/2 1 1 0 0  1/4 1 1/3 0 0 0 0  Groups  Contrast Coefficients:  -1 -1 1/3 0 0 1 0  1/4 0 1/3 -1 0 0 1  1/4 0 -1/2 0 -1 -1 -1  F 0.30 0.03 0.30 0.01 3.82 0.15 3.68  omnibus F (4,726) = 1.70, p=0.147 * p<0.10, F' = 7.88  Hypothesis 14(b): There are no significant differences in achievement among the grou that respond in four different ways regarding "where" Pascal was learned. For those students who were previously knowledgeable in Pascal no significant differences were observed at the 0.05 level among the "places" where various groups of students had learned Pascal (F=2.38, p=0.07). See Table 28. Hypothesis 14(b) was not rejected.  Results  Page 90  Table 28 Comparison of Results Based on "Where" Pascal Language had been Previously Learned Groups  Job  Home  School  School and Home  Total  Number of Students  2  21  286  21  330  Actual Mean  80.7  76.9  71.3  73.8  71.9  Adjusted Mean  79.3  77.2  71.2  75.0  Contrast Coefficients:  -1 0 0 1 0 0  1/3 -1 0 0 1 1  1/3 0 -1 -1 -1 -1/2  1/3 1 1 0 0 -1/2  F 0.31 0.33 1.97 0.89 4.79 1.87  omnibus F (3,325) = 2.38, p=0.07 * p<0.10, F' = 6.33 Results of Achievement  Predictors  The final question is concerned with checking for a linear relationship between the computer science achievement and a set of predictor variables. Achievement  Predictors  Which factor(s) under investigation is/are the best predictor(s) of success in university introductory courses in computer science? Hypothesis 15: Success in introductory computer science courses is unrelated to background characteristics. The percentage earned in all other courses, the year of studies enrolled in, age, gender, the number of computer languages previously learned, the number of computer courses presently enrolled in, whether any computer language had been learned, whether BASIC language had been learned, the order in which BASIC was learned and how well BASIC had been learned, were used as predictor variables in a multiple linear regression equation.  Results  Page 91  Of these variables only the overall year percent (PERCENT) and how well BASIC had been learned (BHOWELL) were found to be significant predictors of achievement (F=306.992, p<0.0001). See Table 17 in Appendix C. A total of 34.3% of the variance in achievement was accounted for by these two variables. The prediction equation for achievement (MARK) was: MARK = 23.862 + 0.633(PERCENT) + 1.827 (BHOWELL) Hypothesis 15 was rejected.  Page 92  Chapter Five  Discussion  This study was undertaken to determine how prior knowledge of BASIC language affects achievement in introductory computer science courses at university. It also compared achievement among groups which differed in gender, age, Faculty and major. The study assessed the differences in achievement in first year FORTRAN and Pascal courses and in a second year computer science course among groups with different computer language backgrounds. It ascertained the impact on achievement of "how well," "when" and "where" a previous computer language was learned. The study identifies factors that are the best predictors of success in introductory courses of computer science. A total of 1194 university students who responded to a questionnaire and completed an introductory computer science course provided the data for the study. These data were analyzed using an analysis of covariance procedure for each of the independent variables involved. The results of the analyses reveal a relationship between prior knowledge of BASIC language and achievement in introductory university computer science courses.  Summary and Conclusions The following paragraphs present a summary of the findings together with conclusions based on these findings. Limitations of the study are indicated. Finally, implications of the results are discussed and suggestions for future research are presented. BASIC Language  Backgrounds  The results showed that the group of students whose first computer language was BASIC achieved significandy better marks than the group who had no prior knowledge of a computer language. This finding suggests that:  Discussion  Page 93  Students who have taken BASIC do better in university introductory level computer science courses than those who have no prior knowledge of a computer language. This finding is not unexpected in view of the fact that, in general, prior related-knowledge enhances further learning. However, it is in disagreement with the opinions of some computer educators referred to in Chapter Two. Other languages were not examined individually to see how previous knowledge of them compared to no prior knowledge of a computer language. These comparisons were not done because this study is primarily interested in how the learning of BASIC affects further achievement in computer science courses. For a language like Pascal to have an influence on achievement in introductory level university computer science courses it may have to be learned in more depth than BASIC language (see page 99). Relatively few of the sample in this study knew Pascal well. The group who had learned either one, two, three or four previous languages, had significantly greater achievement than the group with no prior language experience. The group who had previously learned two or more languages had significantly greater achievement than the group who had learned fewer than two languages. The group who had previously learned a computer language achieved significantly better than the group who had not learned a language. In general, the findings lead to a conclusion that: Students who have previously learned a computer language have better achievement in an introductory computer science course than those who have not learned a language. Also, students with a knowledge of at least two languages have an even higher achievement in introductory computer science. This conclusion would suggest that there are some skills which overlap languages which when learned with the first language can be transferred to the learning of additional languages.  Discussion  Page 94  In concurrence with this conclusion, the group who had learned BASIC and at least one other language achieved significantly better than the group who had learned BASIC language only. A significant difference in achievement was found between the group with prior knowledge of BASIC and the group with previous knowledge of another language but no prior knowledge of BASIC. This finding leads to the conclusion that:  Prior knowledge of BASIC is as beneficial to achievement in university introductory computer science courses as prior knowledge of any other language identified in this study. The number of languages learned may contribute to the achievement difference. While 50% of the students who had learned BASIC had learned two or more languages, 42% of those who had learned another language had knowledge of more than one language. For students who had previously learned only one language an analysis of covariance revealed no significant difference in achievement (F=0.12, p=0.734) between those who had learned BASIC and those who had learned some other language. No significant differences in achievement were found between the group who had learned BASIC as their first language and the group who had learned BASIC other than as their first language. This finding suggests that:  The order in which BASIC is learned may not be a critical factor in subsequent achievement in computer science. While the sequence in learning computer languages is likely to be educationally significant, in this study the sequence was not found to be statistically significant; this may be attributable, at least in part, to the relatively small number of students who learned BASIC after the prior learning of another language. For those questions about BASIC language backgrounds no differences were observed between the group enrolled in the FORTRAN (CPSC 101 and CPSC 151) courses and the  Discussion  Page 95  group enrolled in the introductory Pascal (CPSC 114 and CPSC 118) courses. That is, differences that were found to be significant among the groupings of FORTRAN students were also found to be significant among the same groupings of Pascal students. Demographic Backgrounds It was found that across all six courses males had higher computer science marks than females.  In general, males have greater achievement than females in computer science courses. This finding may be due to the fact that, in general, males in this study had a "greater" entry level background of computer languages than females. It may also be due to the mark differences between courses, or between Faculty entry requirements (as mentioned below). The youngest age group (ages 17-19) had significantly better achievement than the oldest group (ages 25 and over) and had significantly better achievement than the other two age groups combined.  Younger university students tend to have higher achievement than older students in introductory computer science courses. This result may be attributable to the fact that the youngest age group has only recently acquired their computer background while the older groups may have forgotten the computer science material they learned much earlier, or because of the ever-changing content in computer science, perhaps the material learned by the older groups has been drastically revised. Furthermore, the younger students may have had a greater opportunity to use computers than older students. Students from the Faculty of Applied Science had significantly greater achievement than students from all of the other Faculties within this study. This finding may be due, at least in part, to the preponderance (87.9%) of males within the Faculty of Applied Science.  Discussion  Page 96  Students from the Faculty of Commerce and Business Administration earned significandy higher marks in computer science courses than students from all of the other Faculties, excluding the Faculty of Applied Science. It should be noted that students admitted to the Faculty of Applied Science and the Faculty of Commerce and Business Administration have higher admission requirements than do the Faculties of Arts, Education, and Science. The Faculty of Applied Science requires students entering from first year Science to have achieved at least 60% in each of Mathematics, Physics and Chemistry. The minimum standing for admission to the Faculty of Commerce and Business Administration is 60% in pre-Commerce studies. The other three Faculties mentioned have a 50% minimum entrance requirement in all courses. Inter-faculty comparisons are detailed in Chapter Four. They reveal, for example, that Faculty of Science students score significandy higher than Faculty of Arts students.Once again, the preponderance (85.2%) of males within the Faculty of Science may account, at least in part, for the superior performance over students in the Faculty of Arts (males, 51%). The results of these inter-faculty comparisons lead to the conclusion that:  There are differences among Faculties in achievement in introductory computer science courses. The results of inter-faculty comparisons must be interpreted carefully since different courses and different instructors were involved. The group of students majoring in mathematics had significandy higher grades in computer science than the group majoring in other fields within the Faculty of Arts.  Students majoring in mathematics outperform students taking non-mathematics majors within the Faculty of Arts in introductory computer science courses.  Discussion  Page 97  The requirement that mathematics courses be included in the degree program of all students majoring in computer science is evidence that mathematical ability is considered to be an aptitude for subsequent learning in computer science.  Different Language Backgrounds An analysis of achievement in first year FORTRAN courses, first year Pascal courses, and a second year computer science course for each of five student groups, viz., students that had learned: a. only the BASIC language b. BASIC first and at least one other language c. some other language first and BASIC later d. at least one language but not BASIC e. no language found that there were significant differences in first year FORTRAN courses: 1 between the students that had learned only the BASIC language and the students that had not learned any language, (a versus e) 2  between the students that had learned BASIC first and at least one other language and the students that had not learned any language at all, (b versus e) and  3  between the students who knew a language and those who did not know any language. (a,b,c,d versus e)  The students that had not learned any language had significantly lower achievement. These findings suggest that:  Students having a knowledge of any computer language perform at a higher level in first year FORTRAN courses than others who have not learned any computer language. Similarly, in first year Pascal courses the students in each of the first-named groups significantly outperformed the corresponding second group: 1 between the students that had learned only the BASIC language and the students that had not learned any language, (a versus e)  Discussion  Page 98  2  between the students that had learned BASIC first and at least one other language and the students that had not learned any language, (b versus e)  3  between the students that had learned some other language first and BASIC later and the students that had not learned any language, (c versus e)  4  between the students that had learned BASIC first and at least one other language and the students that had learned only the BASIC language, (b versus a) and  5  between the students who knew a language and those who did not know any language. (a,b,c,d versus e)  These findings suggest that: Students having a knowledge of any computer language perform at a higher level in first year Pascal courses than other students who have not learned any computer language.  Thesefindingsfor both FORTRAN courses and Pascal courses further substantiate the earlierfindingsthat students with a knowledge of a computer language have higher achievement than those with no previous knowledge of a computer language. In the second year computer science course no significant differences were found based on the different language backgrounds of the students, (i.e. the languages learned or the order in which they are learned). Thesefindingssuggest that: Achievement in the second year computer science course does not appear to be dependent upon the computer language backgrounds prior to entering university.  In summary, prior knowledge of BASIC language enhances the achievement in first year computer science courses, generally, and in first year Pascal courses the knowledge of another computer language besides BASIC further enhances achievement. BASIC and Pascal  Backgrounds  For students with a prior knowledge of BASIC the group who indicated that they were "able to write complex programs" had significandy higher achievement than the groups who were "vaguely familiar", "capable of reading", or "able to write simple programs" in  Discussion  Page 99  BASIC language. On the other hand, for students with a prior knowledge of Pascal there were no significant achievement differences found among the groups based on "how well" Pascal language had been learned. It should be noted that these results were unchanged in separate analyses of achievement in introductory Pascal and FORTRAN courses. These findings suggest that: Students who are able to write complex BASIC programs perform at a higher level in introductory computer science courses than others who have a limited familiarity with the BASIC language. and that: For students who had prior knowledge of Pascal language the level of achievement in introductory computer science courses did not appear to be dependent upon how well that language had been learned. These conclusions indicate that if previous knowledge of BASIC language is to be of maximum benefit to future computer science course achievement then BASIC must be welllearned. Since the level to which both BASIC and Pascal had been previously learned was stricdy a subjective measurement these conclusions must be interpreted cautiously. For the students who previously knew either BASIC or Pascal language there were significant differences in achievement among the three age groups selected to answer "when" BASIC or Pascal language had been learned. Students in the age group 13 to 18 had significantly higher achievement than those in the age group 18 or more. These findings suggest that: The age at which BASIC or Pascal is first learned is a critical factor and that students in introductory computer science courses who first learned the language in the 13 to 18 age range outperform others who first learned the language at an older age. This outcome may be due to these students having used the language for a longer period of time prior to university, and/or having learned other computer languages in the intervening years.  Discussion  Page 100  There were no significant differences in achievement among the four groups chosen to answer "where" either the BASIC or Pascal language had been learned. This finding suggests that:  The place where either BASIC or Pascal is learned does not appear to be a critical factor for achievement in introductory computer science courses. In summary, the level to which BASIC language had been learned and the age at which both BASIC and Pascal had been learned proved to be significant factors in computer science achievement. The place at which both BASIC and Pascal had been learned and the level to which Pascal had been learned proved not to be significant factors in computer science achievement. Achievement  Predictors  Of the ten variables used as predictors of achievement in a multiple linear regression equation only the overall year percent in all other courses taken (PERCENT) and the variable on "how well" BASIC had been learned (BHOWELL) were found to be significant predictors of achievement. However, only 34% of the variance in achievement was accounted for by these two variables. The prediction equation for achievement was: Achievement = 23.862 + 0.633 (PERCENT) + 1.827 (BHOWELL)  Implications The teaching of BASIC language prior to university appears to be beneficial and should be continued. This is contrary to the belief held by some computer science educators cited in Chapter Two. The results of this study indicate clearly that a previous knowledge of BASIC language is preferable to no knowledge of a computer language when the achievement in introductory computer science courses is being compared. Furthermore,  Discussion  Page 101  the advantage of having previous knowledge of the BASIC language is enhanced by having the knowledge of another language. It should be noted also that students who have acquired the skill to write complex programs in BASIC outperform others who have only a limited familiarity with the language. The findings imply that the order of learning BASIC language and the place of learning BASIC do not affect subsequent achievement in computer science. This study has focused on factors of statistical significance. Attention should also be directed towards their educational significance. Clearly, there are some factors in this study that failed to stand up to the statistical test and the reader may want to consider the educational implications, if any, of these aspects of achievement being considered. For example, the sequence in learning computer languages may be educationally, significant. Similarly, some factors which were statistically significant may not be of educational significance, e.g. some of the differences in results between Faculties may not be educationally significant because of other factors mentioned earlier. Limitations A limitation of this study is that it is being conducted at only one university, and in only one provincial educational system and for just one school year. Furthermore, the sample selected may not best represent the population of people who have knowledge of computer languages. Responses were not received from 365 students (22.8%) of the 1602 who received a course mark. Perhaps the questionnaire should have been administered later in each term after the course change deadline date, so as to have included an even larger percentage of students. Another limitation of the study is that students did not randomly select the courses involved. For example, CPSC 151 enrolled exclusively Faculty of Applied science stu-  Discussion  Page 102  dents, and 79% of the students in CSED 217 were from the Faculty of Education. For this reason, inter-faculty comparisons of achievement have been interpreted conservatively. Another potential problem is that the subjects may be assigned final grades on somewhat different bases. Thus, it is reasonable to assume that there will be some differences in the student achievement attributable to the differences in grading practices between courses or among instructors of any one course. The group of students who entered the university with no knowledge of any computer language may not have had as much interest in computers as that of the group who have learned at least one language already. The group of students enrolled in the second year computer science course are likely to be those who have performed well in the first year course(s), have a "keen" interest in computers, and may be planning to major in computer science. Achievement is probably influenced by the interest shown in the subject and affected by the mark earned in a prerequisite course.  Suggestions for Further Research The study needs to be replicated and expanded to include students from more than just one provincial school system, more than one university and more than one school year. It could include those who program computers who have never enrolled in university computer science courses. It could perhaps involve students from first year computing courses and continue with students in computing courses from each year until graduation. It might also include professional programmers with many years of computer experience. (For such a study, a different measure of achievement would have to be employed). This might well provide insight as to whether the alleged "set of bad programming habits"fromlearning BASIC first can be overcome. There is also a pressing need to embark on similar studies to assess the impact of prior learning of other competing initial languages (such as Pascal, Comal, C, etc.) on subse-  Discussion  Page 103  quent achievement in computer science, and to compare the benefits with those found with BASIC. Further study is desirable to compare gender differences in achievement in computer science courses, since in this study males were found to have significantly higher achievement in first year courses, but not in the second year (CPSC 210) course. This finding may be due to the relatively small number of females (11) enrolled in the course. This study can provide information that may be useful in designing subsequent research about the relationships between various factors and computer science achievement. This study may suggest covariates of achievement that might be selected for further investigation. In view of the above limitations this investigation should be considered exploratory and the findings tentative. In the opinion of the author the apparent benefits of a prior orientation with the BASIC language are sufficiently worthwhile to merit further research.  Page 104  Bibliography Adams, J.M., and Haden, D.H., Introductory service courses in the Computer Science curriculum. ACM SIGCSE Bulletin. Mar. 1972,4(1), 49-52. Agee, R., Questions & answers. Computers in Education. June 1985, 2(10), p. 13. 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June 1970, 24(6), 21-23.  Appendix A  Page 117  Appendix Letter to  A  Instructors  ERIC H A M B E R  SECONDARY  SCHOOL  1985 Dear  I course  am  writing  thi3 I  will  be a t your  class  and would distribution I  A upon  would  copy  i n your  I made  computer  appreciate  on  with  youi n  science  , Sept.  appreciate  and completion  f o ri n c l u s i o n  copy i t s  Thank project.  t h e arrangements  a questionnaire  month.  convenience,  A  t o confirm  t o conducting  in  you  12  S i r :  regards  the  Sept.  of this  a copy  f i v e  o f your  o f this  a t  of class  time f o r  questionnaire.  i n an appendix  o f the results  minutes  course  outline,  a t your  o f the thesis.  study  will  b e made  available  t o  completion. you very  I f you have  much  f o rt h e a s s i s t a n c e  any questions,  of the questionnaire  similar  please  you have  given  do n o t h e s i t a t e  t o the final  draft  i s enclosed.  Sincerely,  David  to this  t o c a l l  N.  E l l i s  me.  Appendix B  Page 119  Appendix B Coding Scheme  Page 120  Appendix B  Coding Scheme  Record 1 Variables  Record 2 Columns  Record Type ("blank") 1 Student Number 2-9 Course Count (0-25) 10-11 Year Standing 12 Year Percent (999.99) 13-17 Status (l-9,blank) 18 Status Date (YYMMDD) 19-24 Number of C.S. courses (1-6) 26 C.S. Course Number (1) 28-30 Term (1 or 2) 32 Mark Obtained ( /75) 34-36 Course Standing 38 C.S. Course Number (2) 41-43 Term 45 Mark Obtained 47-49 Course Standing 51 C.S. Course Number (3) 54-56 Term 58 Mark Obtained 60-62 Course Standing 64 C.S. Course Number (4) 67-69 Term 71 Mark Obtained 73-75 Course Standing 77 C.S. Course Number (5) 80-82 Term 84 Mark Obtained 86-88 Course Standing 90 C.S. Course Number (6) 93-95 Term 97 Mark Obtained 99-101 Course Standing 103  Variables  Columns  Record Type ("*") Student Number Course Section Year Age Sex Faculty Majors Never learned language? Language (BASIC) Order of learning How well When? Where? Language (Pascal) Order of learning How well When? Where? Language (LOGO) Order of learning How well When? Where? Language (Other) Order of learning How well When? Where?  1 2-9 10-12 13-14 15 16-17 18 19-20 21-22 23 24 25 26 27-29 30-33 34 35 36 37-39 40-43 44 45 46 47-49 50-53 54 55 56 57-59 60-63  Page 121  Coding Information  Record 2 Variable Year Sex  Column 15 18  Value 0-9  Meanina 0 for more than 9 years  0 1  Female Male  Faculty  19-20  01 02 03 04 05 06 07 08 09 10 99  Science Arts Commerce and Business Administration Applied Science Forestry Education Agriculture Home Economics Physical Education Music Unclassified  Major(s)  21-22  00 01 02 03 04 05 06 08 09 10 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 29 31 32 33 41  Unknown Computer Science Mathematics Commerce Engineering - all branches Physics Chemistry Music Law Geology & Geophysics Marine Biology Comp. Science, Mathematics Pharmacology, Pharmacy Comp. Science, Chemistry Comp. Science, Physics Biochemistry Comp. Science, Biology Biology Comp. Science, Psychology Economics International Relations Geography English Mathematics, Physics Asian Studies Art General Science Marketing Accounting Finance Computer Engineering  Page 122  Coding information  Record 2 Variable  Column  Where did you learn? Other (specify) 33, 43, 53, 63  Other Languages  54  Value 50 51 52 61 62 63 64 65 66 67 68 69 71 72 73 74 75 81 90 91 92 93 94 95 96 97 98 99 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9  Meaning Forestry Harvesting Resource Management Recreation Physical Education Business Education Mathematics, Science Social Studies, Geography Primary Education Social Studies Special Education Oceanography Agricultural Economics Animal Science Plant Science Soil Science Food Science Dietetics Languages, Linguistics Psychology Political Science Sociology Physiology Microbiology Zoology Botany Qualifying General Usage Summer course 6 Friends 2 Adult Cont Educ course 1 Self-taught 2 Practicum 1 Recreational 1 Uncle 2 Not specified 1 Curiousity 1 Assembler Fortran, Watfiv PL/1 Forth Machine CPL Comal COBOL DBASE 2 Other (APL, C, etc.) usage: 6, 3, many 1's  Appendix C  Page 123  Appendix C Statistical Tests  Page 124  Appendix C  Table 1  21 Aug 89 19:11:04 BORDER  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined  Data)  Order o f L e a r n i n g BASIC  VALUE LABEL  FREQUENCY  0 1 2 3 4  440 698 40 12 4  36.9 58.5 3.4 1.0 .3  TOTAL  1194  100 .0  BASIC not l e a r n e d F i r s t language Second language T h i r d language F o u r t h language  MEAN MAXIMUM VALID CASES  .695 4.000 1194  PERCENT  VALUE  STD DEV MISSING CASES  .616 0  VALID PERCENT 36.9 58.5 3.4 1.0 .3  CUM PERCENT 36.9 95.3 98.7 99.7 100.0  100.0  MINIMUM  .000  Appendix C  Page 125  Table 2  21 Aug 89 19:20:08 BHOWELL  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia How w e l l BASIC was  learned VALUE  VALUE LABEL  VALID CASES  2.212 5.000 1194  FREQUENCY  2 3 4 5  447 80 71 202 157 237  TOTAL  1194  0  BASIC not l e a r n e d Vaguely f a m i l i a r Capable o f r e a d i n g W r i t e s simple programs Quite f a m i l i a r W r i t e s complex programs  MEAN MAXIMUM  (Combined Data)  1  STD DEV MISSING CASES  2.001 0  PERCENT 37.4 6.7 5.9 16.9 13.1 19.8 100.0  VALID PERCENT 37.4 6.7 5.9 16.9 13.1 19.8  CUM PERCENT 37.4 44.1 50.1 67 .0 80.2 100.0  100.0  MINIMUM  .000  Appendix C  Page 126  Table 3  21 Aug 89 19:32:01 BWHEN  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined Data)  When BASIC was l e a r n e d  VALUE  VALUE LABEL BASIC not l e a r n e d Age 18 o r more Age 13 t o 18 Age 13 t o 18 & age > 18 Age up t o 13 Age < 13 & age 13 t o 18  MEAN MAXIMUM VALID CASES  7.458 110.000 1194  FREQUENCY  0 1 10 11 100 110  441 103 623 2 20 5  TOTAL  1194  STD DEV MISSING CASES  14.670 0  PERCENT  VALID PERCENT 36.9 8.6 52.2 .2 1.7 .4  36.9 8.6 52 .2 .2 1.7 .4 100.0  CUM PERCENT 36.9 45.6 97 .7 97.9 99.6 100.0  100.0  MINIMUM  .000  Appendix C  Page 127  Table 4  21 Aug 89 19:41:56 BWHERE  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined  Data)  Where BASIC was l e a r n e d  VALUE LABEL  VALUE  BASIC not l e a r n e d Summer Course Friends A d u l t Cont. E d u c a t i o n Self-taught Recreational Uncle Curiousity Job Home Home & F r i e n d s Home & Job School, U n i v e r s i t y S c h o o l & Job S c h o o l & Home School, Home & Other School, Home & Job  MEAN MAXIMUM VALID CASES  542.930 1110.000 1194  FREQUENCY  0 1 2 3 4 6 7 9 10 100 102 110 1000 1010 1100 1101 1110  448 4 1 1 1 1 1 1 3 103 1 1 528 4 91 1 4  TOTAL  1194  STD DEV MISSING CASES  499.308 0  PERCENT  VALID PERCENT  CUM PERCENT  37.5 .3 .1 .1 .1 .1 .1 .1 .3 8.6 .1 .1 44.2 .3 7.6 .1 .3  37 .5 .3 .1 .1 .1 .1 .1 .1 .3 8.6 .1 .1 44 .2 .3 7.6 .1 .3  37.5 37.9 37.9 38.0 38.1 38.2 38.3 38.4 38.6 47 .2 47 .3 47 . 4 91.6 92.0 99.6 99.7 100.0  100.0  100.0  MINIMUM  000  Appendix C  Page 128  Table 5 21 Aug 89 19:46:18 PORDER  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined  Data)  Order o f L e a r n i n g PASCAL  VALUE  VALUE LABEL P a s c a l not l e a r n e d F i r s t language Second language T h i r d language F o u r t h language  MEAN MAXIMUM VALID CASES  .621 4.000 1194  FREQUENCY  0 1 2 3 4  858 54 178 85 19  TOTAL  1194  STD DEV  MISSING CASES  1.074  0  PERCENT  VALID PERCENT  71.9 4.5 14.9 7.1 1.6 100.0  71.9 4.5 14.9 7.1 1.6  CUM PERCENT 71.9 76.4 91.3 98.4 100.0  100.0  MINIMUM  .000  Page 129  Appendix C  Table 6  21 Aug 8 9 19:47:40 PHOWELL  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia How w e l l PASCAL was  learned VALUE  VALUE LABEL P a s c a l not l e a r n e d Vaguely f a m i l i a r Capable o f r e a d i n g W r i t e s simple programs Quite f a m i l i a r W r i t e s complex programs  MEAN MAXIMUM VALID CASES .  .899 5.000 1194  (Combined Data)  FREQUENCY  PERCENT  VALID PERCENT  0 1 2 3 4 5  872 59 34 72 54 103  73.0 4.9 2.8 6.0 4.5 8.6  73.0 4.9 2.8 6.0 4.5 8.6  TOTAL  1194  100.0  100.0  STD DEV MISSING CASES  1. 667 0  MINIMUM  CUM PERCENT 73.0 78.0 80.8 86.9 91.4 100.0  .000  Appendix C  Page 130  Table 7  21 Aug 89 19:48:57 PWHEN  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia When PASCAL was l e a r n e d  VALUE LABEL Pascal Age 18 Age 13 Age 13  (Combined Data)  VALUE  not l e a r n e d o r more t o 18 t o 18 & age > 18  MEAN MAXIMUM VALID CASES  1.399 11.000 1194  FREQUENCY  0 1 10 11  858 188 146 2  TOTAL  1194  STD DEV MISSING CASES  3.262 0  PERCENT  VALID PERCENT  71.9 15.7 12.2 .2 100.0  71. 9 15.7 12.2 .2  CUM PERCENT 71.9 87.6 99.8 100.0  100.0  MINIMUM  .000  Appendix C  Page 131  Table 8  21 Aug 89 19:50:37 PWHERE  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined Data)  Where PASCAL was l e a r n e d  VALUE LABEL  VALUE  P a s c a l not l e a r n e d Not s p e c i f i e d Job Home School, U n i v e r s i t y School & Other S c h o o l & Job S c h o o l & Home  MEAN MAXIMUM VALID CASES  262.344 1100.000 1194  FREQUENCY  0 8 10 100 1000 1001 1010 1100  861 1 2 21 286 1 1 21  TOTAL  1194  STD DEV MISSING CASES  440.477 0  PERCENT  VALID PERCENT  72.1 .1 .2 1.8 24.0 .1 .1 1.8 100.0  72.1 .1 .2 1.8 24.0 .1 .1 1.8  CUM PERCENT 72.1 72.2 72.4 74.1 98.1 98.2 98.2 100.0  100 .0  MINIMUM  .000  P a g e 132  Appendix C  Table 9 21 Aug 89 19:52:10 LORDER  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia Order o f L e a r n i n g  VALID CASES  .147 4.000 1194  FREQUENCY  0 1 2 3 4  1123 9 31 19 12  TOTAL  1194  LOGO not l e a r n e d F i r s t language Second language T h i r d language F o u r t h language  MEAN MAXIMUM  Data)  LOGO VALUE  VALUE LABEL  (Combined  STD DEV  MISSING CASES  . 628  0  PERCENT  VALID PERCENT 94 .1 .8 2.6 1.6 1.0  94.1 .8 2.6 1.6 1.0 100.0  CUM PERCENT 94.1 94.8 97 .4 99.0 100.0  100.0  MINIMUM  .000  Appendix C  Page 133  Table 21 Aug 8 9 19:54:24 LHOWELL  10  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined Data)  How w e l l LOGO was l e a r n e d  VALUE LABEL  VALUE  FREQUENCY  0 1 2 3 4 5  1123 23 14 26 6 2  TOTAL  1194  LOGO not l e a r n e d Vaguely f a m i l i a r Capable o f r e a d i n g W r i t e s simple programs Quite f a m i l i a r W r i t e s complex programs  MEAN MAXIMUM VALID CASES  . 137 5.000 1194  STD DEV MISSING CASES  .605 0  PERCENT  VALID PERCENT  94.1 1.9 1.2 2.2 .5 .2 100.0  94..1 1..9 1.,2 2..2 .5 .2  CUM PERCENT 94.1 96.0 97.2 99.3 99.8 100.0  100,.0  MINIMUM  .000  Page 134  Appendix C  T a b l e 11 21 Aug 89 19:55:53 LWHEN  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined Data)  When LOGO was l e a r n e d  VALUE LABEL LOGO not l e a r n e d Age 18 o r more Age 13 t o 18  MEAN MAXIMUM VALID CASES  .518 10.000 1194  VALUE  FREQUENCY  0 1 10  1124 9 61  TOTAL  1194  STD DEV  MISSING CASES  2 .203  0  PERCENT  VALID PERCENT  94.1 .8 5.1 100.0  94.1 .8 5.1  CUM PERCENT 94.1 94.9 100.0  100.0  MINIMUM  .000  Page 135  Appendix C  T a b l e 12 21 Aug 89 19:58:24 LWHERE  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia Where LOGO was  VALUE LABEL LOGO not l e a r n e d Summer Course Practicum Job Home Home & Other School, U n i v e r s i t y S c h o o l & Home  MEAN MAXIMUM VALID CASES  37.050 1100.000 1194  (Combined  Data)  learned VALUE  FREQUENCY  0 1 5 10 100 101 1000 1100  1127 2 1 3 18 1 39 3  TOTAL  1194  STD DEV MISSING CASES  185.794 0  PERCENT  VALID PERCENT  94.4 .2 .1 .3 1.5 .1 3.3 .3 100.0  94.4 .2 .1 .3 1.5 .1 3.3 .3  CUM PERCENT 94.4 94.6 94.6 94.9 96.4 96.5 99.7 100 .0  100.0  MINIMUM  .000  \  Page 136  Appendix C  T a b l e 13 21 Aug 89 19:59:49 OORDER  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia Order o f L e a r n i n g  Other  MEAN MAXIMUM VALID CASES  .542 4.000 1194  FREQUENCY  0 1 2 3 4  896 63 141 74 20  TOTAL  1194  Other not l e a r n e d F i r s t language Second language T h i r d language F o u r t h language  Data)  language  VALUE  VALUE LABEL  (Combined  STD DEV  MISSING CASES  1.029  PERCENT  VALID PERCENT  75.0 5.3 11.8 6.2 1.7 100.0  75 .0 5 .3 11 .8 6 .2 1 .7  CUM PERCENT 75.0 80.3 92.1 98.3 100.0  100 .0  MINIMUM  .000  Appendix C  Page 137  Table  21 Aug 89 20:01:31 OHOWELL  14  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined Data)  How w e l l Other language was l e a r n e d VALUE  VALUE LABEL  VALID CASES  .802 5.000 1194  PERCENT  VALID PERCENT  0 1 2 3 4 5  908 42 22 70 98 54  76.0 3.5 1.8 5.9 8.2 4.5  76.0 3.5 1.8 5.9 8.2 4.5  TOTAL  1194  100.0  100.0  Other not l e a r n e d Vaguely f a m i l i a r Capable o f r e a d i n g W r i t e s simple programs Quite f a m i l i a r W r i t e s complex programs  MEAN MAXIMUM  FREQUENCY  STD DEV MISSING CASES  1.562 0  MINIMUM  CUM PERCENT 76.0 79.6 81.4 87.3 95.5 100.0  .000  Appendix C  Page 138  Table 15  21 Aug 89 20:02:57 OWHEN  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia When Other language was  VALUE LABEL Other not Age 18 o r Age 13 t o Age 13 t o Age up t o  learned more 18 18 & age > 18 13  MEAN MAXIMUM VALID CASES  1.214 100.000 1194  (Combined Data)  learned  VALUE  FREQUENCY  PERCENT  0 1 10 11 100  897 179 116 1 1  75.1 15.0 9.7 .1 .1  TOTAL  1194  STD DEV MISSING CASES  4.109 0  100.0  VALID PERCENT 75 .1 15 .0 9 .7 .1 .1  CUM PERCENT 75.1 90.1 99.8 99.9 100.0  100 .0  MINIMUM  .000  Appendix C  Page 139  T a b l e 16  21 Aug 89 20:04:08 OWHERE  COMPUTING LANGUAGES - P r e l i m i n a r y T e s t i n g U n i v e r s i t y o f B r i t i s h Columbia  (Combined  Data)  Where Other language was l e a r n e d  VALUE LABEL  VALUE  Other not l e a r n e d Summer Course Job Home Home & Job School, U n i v e r s i t y S c h o o l & Job S c h o o l & Home S c h o o l , Home & Job  MEAN MAXIMUM VALID CASES  200.696 1110.000 1194  FREQUENCY  PERCENT  VALID PERCENT  0 1 10 100 110 1000 1010 1100 1110  899 1 8 52 1 219 2 10 2  75 .3 .1 .7 4 .4 .1 18 .3 .2 .8 .2  75 .3 .1 .7 4 .4 .1 18 .3 .2 .8 .2  TOTAL  1194  100 .0  100 .0  STD DEV MISSING CASES  397.018  MINIMUM  CUM PERCENT 75.3 75.4 76.0 80.4 80.5 98.8 99.0 99.8 100.0  .000  Appendix C  Page 140  Table 17 21 Sep 89 17:04:36  COMPUTING LANGUAGES - P r e l i m i n a r y U n i v e r s i t y o f B r i t i s h Columbia * * * »  Testing  (Combined  M U L T I P L E  Data)  R E G R E S S I O N  L i s t w i s e D e l e t i o n o f M i s s i n g Data Mean MARK NOLANG NOCSCOUR BORDER BASIC AGE SEX YEAR PERCENT BHOWELL NVRLRND  S t d Dev  68 .953 1 .233 1 .362 .696 .636 20 . 177 .723 1 .833 64 .845 2 .219 .301  N o f Cases -  14 .769 1 .111 .686 .617 .481 3 .404 .448 1 .073 12 .588 2 .002 .459  Label Mark i n S u r v e y e d C o u r s e Number o f l a n g u a g e s p r e v i o u s l y l e a r n e d Number o f Computer C o u r s e s Taken O r d e r o f L e a r n i n g BASIC Was BASIC l e a r n e d ? Age o f s t u d e n t i n y e a r s Gender o f s t u d e n t U n i v e r s i t y year e n r o l l e d i n Year Percent How w e l l BASIC was l e a r n e d Never L e a r n e d Language?  1178  Correlation:  MARK NOLANG NOCSCOUR BORDER BASIC AGE SEX YEAR PERCENT BHOWELL NVRLRND  MARK  NOLANG  NOCSCOUR  1.000 .200 .010 .152 .185 -.043 .032 -.032 .531 .229 -.195  .200 1.000 .401 .645 .691 -.106 .206 -.052 -.022 .717 -.728  .010 .401 1.000 .261 .204 .020 .122 .073 -.064 .235 -.214  BORDER  * Equation  Number 1  Dependent  Variable.  B e g i n n i n g B l o c k Number 1. Method: NOLANG NOCSCOUR BORDER BASIC Variable(a)  E n t e r e d on S t e p Number  Multiple R R Square A d j u s t e d R Square Standard E r r o r  AGE  SEX  -.043 -.106 .020 - .147 -.244 1 .000 .029 .479 .031 -.280 .129  .032 .206 .122 .143 .127 .029 1.000 -.058 -.118 .192 -.166  SEX  PERCENT  Analysis  .53099 .28195 ,28134 12.52040  . 185 .691 .204 .856 1.000 -.244 .127 -.194 -.058 .839 -.866  YEAR  PERCENT  BHOWELL  .531 -.022 -.064 -.053 -.058 .031 -.118 .078 1.000 -.034 .044  .229 .717 .235 .701 .839 -.280 .192 -.206 -.034 1.000 -.727  -.032 -.052 .073 -.096 -.194 .479 -.058 1.000 .078 -.206 . 104  NVRLRND -.195 -.728 -.214 -.741 -.866 . 129 -.166 .104 . 044 -.727 1.000  M U L T I P L E R E G R E S S MARK Mark i n S u r v e y e d Course  Stepwise AGE 1.  BASIC  . 152 .645 .261 1.000 . 856 -.147 .143 -.096 -.053 .701 -.741  BHOWELL Year  PERCENT  NVRLRND  Percent  o f Variance  Regression Residual  Sum o f S q u a r e s 72386.04905 184350.24071  1 1176  Mean S q u a r e 72386.04905 156.76041  Signif F Variables Variable PERCENT (Constant)  Variable(s)  SE B  B .622987 28.555214  E q u a t i o n Number 1  i n the Equation  .028991 1.915026  Dependent  .58584 .34320 .34208 11.97954  T  .530987  Variable  E n t e r e d on S t e p Number  Multiple R R Square A d j u s t e d R Square Standard E r r o r  Variables  Beta  2.  «  Sig T  21..489 14..911  Variable  .0000 .0000  NOLANG NOCSCOUR BORDER BASIC AGE SEX YEAR BHOWELL NVRLRND  Beta I n  not i n t h e Equation  Partial  .211303 .249302 .044082 .051916 .180484 .212695 .216732 .255338 -.059078 -.069686 .096322 .112874 -.073618 -.086612 .247641 .292076 -.219176 -.258396  Min  Toler .999531 .995932 .997230 .996644 .999067 .986032 .993899 .998853 .998027  T  Sig T  8..824 1..782 7..462 9..053 -2..395 3..894 -2..980 10..468 -9.. 169  .0000 .0750 .0000 .0000 .0168 .0001 .0029 .0000 .0000  M U L T I P L E R E G R E S S I O N MARK Mark i n S u r v e y e d C o u r s e BHOWELL  Analysis  How w e l l BASIC was l e a r n e d  o f Variance DF 2 1175  Regression Residual  Sum o f S q u a r e s 88112.63255 168623.65720  Mean S q u a r e 44056.31628 143.50950  Signif F Variables Variable PERCENT BHOWELL (Constant)  i n the  V a r i a b l e s n o t i nt h e  B  SE B  Beta  T  Sig T  Variable  .632829 1.827271 23.862283  .027755 . 174552 1.. 886345  .539375 .247641  22. 801 10. 468 12. 650  .0000 .0000 .0000  NOLANG NOCSCOUR BORDER BASIC AGE SEX YEAR NVRLRND  ......  *  *  Beta I n  Partial  .069537 .059788 -.014539 - .017410 .013565 .011931 .029284 .019612 .011043 .013076 .050946 .061293 -.023916 - .028800 -.082973 - .070274  . . . .. . . . . . . . . « . . . .  Min  Equation Toler  T  Sig T  .485206 .941777 .508125 .294575 .920712 .950667 .952447 .471145  2,.052 ,597 ,409 .672 .448 2,.104 ,987 -2..414  .0404 .5509 .6827 .5017 .6542 .0356 .3237 .0159  -,  -,  Appendix D  Page 141  Appendix D Personal Communications  Educational  Technology  Computers and -Learnin or Don't Teach BASIC  Alfred Bork  .This column proposes that it is a mistake to teach BASIC. A t least I claim thac it is a mistake as a first language, and perhaps it is a mistake more generally. (Actually, I know o f few examples where BASIC is taught as a second language, except for selfteaching.) This anti-BASIC position will undoubtedly be viewed as heresy by many readers, as BASIC is a very commonly taught first language at the present time. In the first section of  Alfred: Bork, Professor of Physics and Professor of Information and Computer Science, is Director of the Educational Technology Center at the University of California at Irvine. The Center is working in the area of personal computers, and projects are proceeding at this time in public libraries and middle schools. Dr. 8ork was one of four American speakers to the most recent World Conference on Computers in Education. He was a co-director and keynote speaker at a recent N A T O Advanced Study Institute and a consultant to the UK National Development Programme in Computer Aided Learning. He has served as chair of the Special Interest Group on Computer Uses in Education of .the Association for Computing Machinery and as Physics Series Editor for CONDUIT. Dr. Bork has published over 30 papers during the past three years and has written chapters in five books. He edited Computer Assisted Learning in Physics Education (Pergamon Press, 1980) and is the author of Learning with Computers (Digital Press, 1981).  this column, I will comment on why I believe this is an important step to take at the present time. In the second section, I will comment on some possible objections to this position. Why Shouldn't We Teach BASIC? When languages such as BASIC and F O R T R A N were developed, there was little experience with programming, particularly for large, complex activi, ties. However, this is no longer the case. Today we have much experience with writing elaborate programs. Our experience has led to a series of strategies which make it easier to write such programs with fewer errors, and easier to revise these programs. While not everyone is going to write complex programs, many people in the future will be involved in this activity. Indeed, all projections indicate that we will not have enough programmers, given our current ways of producing programmers, to meet this need. The set of ideas that has evolved for good programming practice sometimes goes under the name of "structured programming." A variety of factors are involved, which I will not attempt to review. Components of software engineering also play a very important role. BASIC,, because it does not lead easily to structured programming, tends to develop poor programming  EDUCATIONAL TECHNOLOGY/April, 1982  Volume  XXII,  Columnists^ habits, particularly as it is almost universally taught. These programming habits are very difficult for students to overcome later. We have not done students a service if we teach them fundamental ideas and ingrained ways of working which they later must destroy, particularly if these are the first ideas that they encounter in the area. It is difficult to correct such early habits. So BASIC hurts students in the long run, in spite of any short-run- advantages (and, as will be seen, I claim that there are not really any short-range advantages either!). We' have seen many students at Irvine who have learned BASIC in high school or on their own who have considerable difficulty in the beginning programming courses. Our situation is not unique. For example, I have heard of very similar cases at the Air Force Academy in Colorado Springs. It is not easy to overcome some ingrained habits, particularly if they have been ingrained for a long time. Another factor to be considered is the lack of standardization in existing BASICs. Although there is a BASIC, standard underway, I do not see any widespread move of existing BASICs, or even newly developed BASICs, to conform to that standard. In fact, it seems extremely unusual that a standard for an existing language should depart so much from the current, implementations of the language. Finally, and perhaps the most critical point, there are better languages for the student to begin with. The languages in the A L G O L family, such as Pascal, do allow a natural approach to structured programming. They too are not always taught in this fashion, unfortunately, because often the textbooks are written by people with the same bad habits I referred to above! But the percentage is certainly much better; that is, more people learn to program in a satisfactory fashion with Pascal, for example, than with a language such as BASIC. The coming likely importance of Ada is also an important factor to consider. If Ada becomes as widely  Number  4  33  used as seems likely, given the strong interest of the Department of Defense, it is likely to require a whole new generation of programmers to work on it. It is very hard to imagine that students brought up on BASIC are likely to become good Ada programmers. Comments on Possible Objections There are a number of comments which I feel are likely to be made about the position 1 .have just taken. As I indicated, it certainly is not the common position; these views are likely to bring down the wrath of many BASIC users. I will consider some of the criticisms that may appear. The first argument that is often made for BASIC is that it is common, particularly on small microcomputers. This is true. Most microcomputers have some version or other of B A S I C But these versions -differ widely. As already indicated, BASIC is one of the least standardized languages around, with widely varying dialects. This is particularly true when one gets beyond the most elementary level.. But I do not feet that this is a good argument. If we always stuck with what was common in a particular time, a new and better idea would never be used. That is, we would never make any progress. Certainly, at one time F O R T R A N was extremely common, when BASIC was first being introduced. The same argument could have been used at one time by F O R T R A N users to persuade people not to study BASIC. Other languages, including Pascal, are increasingly available. The second reason one often hears for teaching 3ASIC as a first language is the belief that BASIC is easy to teach. I believe that this is an old wive's tale, a position not supported by empirical evidence. As a teacher who has taught many different programming languages, it seems to me that for almost any language a reasonable subset is relatively easy to teach to beginners. The ease depends not so much on the language but on two quite different factors. One of these is the implementation. There are clearly some advantages in teaching an implementation which is based on an interpreter, rather than an implementation that is based on a compiler. Whether one has interpreters or compilers has little to do with the nature of the  34  language in most cases, but only the nature of the implementation. The second important factor in how rapidly beginners become acquainted with the language is the method in which the language is taught. Users can become familiar with the language in widely different amounts of time, depending on just how the language is introduced. Many methods of teaching languages, partic-  ... one often hears... that BASIC is easy to teach. I believe this is an old wive's tale, a position not supported by empirical evidence."  ularly those based on grammatical approaches, waste large amounts of student time, independent of the language being used. But this is a separate topic that cannot be adequately addressed within the present column. Perhaps the most important objection is the one I have left for last. It is often argued, correctly, that one can teach BASIC in a structured fashion. This can certainly be done to some extent. But the truth is that it is almost never done! One can find very few examples of teaching BASIC in this way. Certainly, in the commonly available books which students usually use in learning BASIC, one sees almost no teaching of BASIC in a structured fashion. There are some exceptions to this, but very few. One cannot even introduce BASIC in a structured fashion with many BASICs; that is, one can achieve some aspects of structure with these BASICS, but not others. Many BASICs, for example, have no adequate procedures. The notion of teaching topdown programming without a powerful procedure concept is, it seems to me, ridiculous. An expression that says " G O S U B 9 8 2 " is no substitution for an adequate procedure. A few BASICs do have adequate procedure mechanisms, such as some of the Digital 8ASlCs. But many do not. Another problem with many BASICS, which prevents them from being taught in a structured fashion, is the  use of single letter or single letter plus number identifiers. Thus, one cannot give meaningful names to identifiers, and one teaches students to call identifiers by such names as " X 4 2 , " which have no meaning and which lead to great difficulty when one tries to revise the program at a later time. Final Conclusions I would certainly welcome any comments about these arguments. Perhaps I have missed some critical points. But it is important, particularly given the growing need for competent computer programmers in our society, to give some very careful thought to these issues. We are about to be flooded with new high school courses involving B A S I C There are some alternates to this. There is at least one quite good high school course, " C o m puter Power," based on a variant of Pascal, available from McGraw-Hill. So perhaps there are some rays of hoDe in what appears to be a generally dark night. •  Forthcoming Columns The following columns are some of chose now in preparation for publication in this magazine: • Alfred Bork: Ronald Reagan's Big Mistake. • Leslie ). Briggs A Comment on the Training of Instructional Designers. • James E. Eisele: Programming or Authoring? • Albert l _ Goldberg: The Eclectic Technologist. • M . David Merrill: Authoring Systems: Are They Really? • Dean R. Spitzer: Facilitating Training Results Back on the Job. • Bruce W. Tuckman: "This Is a Recorded Message . . . " Each Educational  Technology  Columnist appears in these pages several times yearly. More than a dozen columnists are writing columns for you. These columns cover the full range of problems, issues, and concerns in the field of educational technology.  I 11  ' !' * ' !  < ,' '' < , |] ,< ' ;  ,• r *  ' < J * J j \  EDUCATIONAL TECHNOLOGY/April, 1982  THE FOURTH REVOLUTION - COMPUTERS AND LEARNING A l f r e d Boric  "Of a l l human inventions since the beginning o f mankind, the microprocessor i s unique. I t i s d e s t i n e d to play a p a r t i n a l l areas of l i f e , without e x c e p t i o n — t o increase our c a p a c i t i e s , to f a c i l i t a t e or e l i m i n a t e tasks, to replace p h y s i c a l e f f o r t , to increase the p o s s i b i l i t i e s and areas of mental e f f o r t , to turn every human being i n t o a c r e a t o r , whose every idea can be a p p l i e d , d i s s e c t e d , put together a g a i n , transmitted, changed. The theme o f t h i s paper i s that we are on the verge o f a major change i n the way people l e a r n . T h i s change, d r i v e n by the personal computer, w i l l a f f e c t a l l l e v e l s o f education from e a r l i e s t childhood through a d u l t education. I t w i l l a f f e c t most s u b j e c t areas and most l e a r n e r s . I t w i l l a f f e c t both education and t r a i n i n g . I t w i l l be one of the few major h i s t o r i c a l changes i n the way people l e a r n . The impact of the computer i n education w i l l not produce an incremental change, a minor a b e r r a t i o n on the c u r r e n t ways o f l e a r n i n g , but w i l l lead to e n t i r e l y d i f f e r e n t l e a r n i n g systems. T h i s massive change i n education w i l l occur over the next twenty years. Schools, i f they e x i s t a t a l l , w i l l be very d i f f e r e n t a t the end o f that period. There w i l l be fewer teachers, and the r o l e o f the teacher w i l l be d i f f e r e n t from the r o l e o f teacher i n our current educational d e l i v e r y system. I use •schools" throughout t h i s paper i n the general sense to i n c l u d e any formal schooling a c t i v i t y , whether i t be the t h i r d grade or the u n i v e r s i t y , or any other l e v e l ; f o r emphasis I sometimes mention p a r t i c u l a r types o f schools. I hasten t o say that t h i s change w i l l not n e c e s s a r i l y be a d e s i r a b l e change. Any powerful technology c a r r i e s w i t h i n i n i t the seeds of good and e v i l , and that a p p l i e s to an e d u c a t i o n a l technology. One o f my major goals i n making p r e s e n t a t i o n s o f t h i s kind i s to nudge us toward a more d e s i r a b l e e d u c a t i o n a l f u t u r e rather than a l e s s d e s i r a b l e educational f u t u r e . Our e f f o r t s i n the next few years are p a r t i c u l a r l y c r i t i c a l f o r education. The f u l l , long-range i m p l i c a t i o n s o f the computer i n our world o f l e a r n i n g are seldom discussed. Indeed, people are o f t e n overwhelmed by the technology, delighted with each new toy which they r e c e i v e . Yet these i m p l i c a t i o n s must be c o n s i d e r e d i f we are to move toward an improvement i n our e n t i r e e d u c a t i o n a l system. The strategy o f t h i s paper w i l l be to f i r s t look a t the ^why," then to look a t the "how," and then to r e t u r n to present a c t i o n . Many o f the issues are discussed i n more d e t a i l i n my recent book, Learning with Computers.^  The  1 U n i v e r s i t y o f Oreeon - June 1982  —  —  —  —  —  WHY WILL THE COMPUTER 3EC0ME THE DOMINANT EDUCATIONAL DELIVERY SYSTEM?  In making a b r i e f case as to why the change I am suggesting w i l l take place, I f i r s t look b r i e f l y a t educational f a c t o r s i n modern s o c i e t y . Then I w i l l consider aspects d i r e c t l y r e l a t e d to the computer. C u r r e n t Status of Education F i r s t , tt dooc not take any great p f f n r r to SPP that nnr. educafcicmal'"By3le.u ia'-curgently in tf o*rb£»r We are being t o l d t h i s c o n s t a n t l y from a l l s i d e s . The d a i l y newspapers, the popular magazines, and recent books are f u l l o f d e s c r i p t i o n s o f the problems o f our current e d u c a t i o n a l systems. One can even measure these to some extent by d e c l i n i n g SAT s c o r e s , d e c l i n i n g s t e a d i l y u n t i l l a s t year, and s i m i l a r r e s u l t s from the N a t i o n a l Assessment tests. 1  Independent of s t a t i s t i c s , however, the most i n t e r e s t i n g and c r i t i c a l information i s the d e c l i n e i n f a i t h i n education i n the United S t a t e s . We can see t h i s very h e a v i l y r e f l e c t e d among p o l i t i c i a n s at a l l l e v e l s . At one time f o r a p o l i t i c i a n to speak out against education was s u i c i d a l . Now we f i n d that i t i s o f t e n p o l i t i c a l l y effective. Indeed, our c u r r e n t president campaigned on the notion that we d i d n ' t need a Department of Education, although so f a r he hasn't a b o l i s h e d i t . But he d i d a b o l i s h the e n t i r e science education d i v i s i o n w i t h i n the N a t i o n a l Science Foundation, simply by c u t t i n g i t s budget e f f e c t i v e l y to zero. The p o l i t i c i a n s know that education has l i t t l e support i n American s o c i e t y and that, indeed, i t i s p o l i t i c a l l y expedient to c u t e d u c a t i o n a l funds. Education has few defenders and many detractors. I do not wish to imply that these problems with education are simply a matter of p u b l i c r e l a t i o n s . Indeed education has very r e a l problems i n t h i s country and elsewhere. In the whole h i s t o r y of the American educational system there has seldom been a time when there was greater t u r m o i l and where the s t a t u s of t e a c h i n g , i n both the p u b l i c schools and u n i v e r s i t i e s , has been lower than i t i s now. A l l i n d i c a t i o n s point to the f a c t that t h i s d e c l i n e i n popular support of our e d u c a t i o n a l system w i l l continue. Few p o s i t i v e f a c t o r s other than i n t e r e s t i n the computer can be pointed t o . Coupled with t h i s d e c l i n i n g a p p r e c i a t i o n o f education, perhaps even a consequence, i s a f a c t o r which a f f e c t s education even more d i r e c t l y , the f a c t o r o f i n c r e a s i n g f i n a n c i a l c o n s t r a i n t s . The schools do not r a i s e enough money to run an adequate educational system i n t h i s country today. Any adequate s c i e n c e or mathematics teacher can make f a r more money o u t s i d e of the schools and u n i v e r s i t i e s than that i n d i v i d u a l can make w i t h i n  153  the schools. A few teachers w i l l be dedicated enough to stay with the schools or to go to schools i n s p i t e of t h i s . But many competent people w i l l not, and many people who are not competent to do anything else w i l l teach. These are harsh statements, ones that are not pleasant to hear, but I think they must be made. F i n a n c i a l c o n s t r a i n t s a l s o show up i n other important ways i n education beside teacher s a l a r i e s . We have had no new major curriculum development at any l e v e l i n the u n i t e d States f o r over ten years. I am r e f e r r i n g to s i z a b l e c u r r i c u l u m development p r o j e c t s , the type which c o u l d lead to improvement i n our educational system. Indeed, s i n c e the development o f the MACOS course i n the e a r l y 1970*s, f e d e r a l funding i n c u r r i c u l u m development stopped almost e n t i r e l y . I r o n i c a l l y , we were j u s t becoming s k i l l f u l i n such development when the funds vanished. What we learned i s now being used i n l a r g e - s c a l e c u r r i c u l u m development i n other c o u n t r i e s . Another dismal f a c t o r i n American education i s the c u r r e n t classroom environment. Even young c h i l d r e n f r e q u e n t l y show l i t t l e i n t e r e s t i n education, r e f l e c t i n g widespread p a r e n t a l a t t i t u d e s . High school c l a s s e s o f t e n seem more l i k e b a t t l e f i e l d s than educational i n s t i t u t i o n s . This i s i n stark c o n t r a s t to what one f i n d s i n many other c o u n t r i e s a t the present time. Hence, American education, and to a l e s s e r extent education everywhere, i s i n t r o u b l e a t the moment. ^F^^^ft^S^SSSfm, aiiJ ijevr^rary^^f"'d(ytng^tyf¥iig^. Much o f the pressure on education i s from the o u t s i d e , and t h i s i s the type of pressure which can lead to r e a l change. "The teaching p r o f e s s i o n i s caught i n a v i c i o u s c y c l e , s p i r a l i n g downward. Rewards are few, morale i s low, the best teachers are b a i l i n g out and the supply of good r e c r u i t s i s drying up." 3  When we move from t h i s dismal p i c t u r e of what i s happening i n education today to look a t the computer s i t u a t i o n , the p i c t u r e i s e n t i r e l y d i f f e r e n t . The computer, the dominant technology o f our age and s t i l l r a p i d l y developing, shows great promise as a l e a r n i n g mode. I t has been s a i d that the computer i s a g i f t of fire. P i r s t , a few hardware comments. Personal computers w i l l be dominant i n education. But i t i s a mistake to b e l i e v e that computers c u r r e n t l y around are the ones I am t a l k i n g about. We are only at the beginning stage of computer development, p a r t i c u l a r l y with regard to the personal computer. Today's Apples and even today's IBM Personal Computers, a good b i t more s o p h i s t i c a t e d than the Apple, are hardly a shadow o f the types o f machines that w i l l dominate l e a r n i n g . C e n t r a l processing u n i t s are becoming cheaper and more s o p h i s t i c a t e d , and memory of a l l  3  types i s r a p i d l y dropping i n p r i c e . The integrated c i r c u i t technology i s only at i t s beginning, and we can expect a long steady d e c l i n e i n p r i c e s , i n c r e a s e i n c a p a b i l i t i e s , and decrease i n s i z e . Going along with t h i s w i l l be increased educational c a p a b i l i t i e s , such as sound (both i n and o u t ) , much better graphics, a l t e r n a t e media, such as those provided by the v i d e o d i s c , and a host of other r a p i d developments. In planning for computers i n education we must give f u l l a t t e n t i o n to t h i s dynamic s i t u a t i o n rather than focusing on today's hardware. Technology i s not l e a r n i n g . We can be too c a r r i e d away with the technology and become i n t e r e s t e d i n i t to the e x c l u s i o n of l e a r n i n g ! So we should not g i v e primary a t t e n t i o n i n education to the new hardware developments. The r e a l i n t e r e s t i n the computer in l e a r n i n g l i e s not i n i t s d e c r e a s i n g p r i c e and i n c r e a s i n g c a p a b i l i t i e s , obvious to a l l , but rather to i t s e f f e c t i v e n e s s as a l e a r n i n g device. How does one demonstrate t h i s e f f e c t i v e n e s s ? In education the t r a d i t i o n a l mode of experiment has seldom proved to be s a t i s f a c t o r y . Neither the f i n a n c i a l resources nor the number of s u b j e c t s are adequate i n most e x i s t i n g educational research. The d i f f i c u l t i e s have to do with the many v a r i a b l e s which cannot be c o n t r o l l e d , so d i f f e r e n t from the experimental s i t u a t i o n s t h a t were t y p i c a l of the p h y s i c a l s c i e n c e s . Few l a r g e - s c a l e experiments have proceeded with the computer, and these were o f t e n flawed. Further, our s k i l l s i n developing materials have advanced, and many of the s t u d i e s are based on minimal e a r l y m a t e r i a l . We can f i n d l i s t s o f research p r o j e c t s that supposedly do or don't demonstrate that the computer i s good i n l e a r n i n g , but I am s i n g u l a r l y unimpressed with most of these studies when I examine them c l o s e l y . So the use of adequate comparison s t u d i e s i n demonstrating t h a t computers are u s e f u l i n education i s seldom p r a c t i c a l . A l l i s not l o s t , however, i n demonstrating e f f e c t i v e n e s s f o r users. One important way to do t h i s , very c o n v i n c i n g i n many s i t u a t i o n s , i s to look at some examples of what i s p o s s i b l e and to p o i n t out the features of those examples which lead to the computer becoming g e n e r a l l y very e f f e c t i v e i n l e a r n i n g . I t i s t h i s approach we w i l l f o l l o w here. Another approach i s through peer e v a l u a t i o n , the examination of materials by p e d a g o g i c a l experts i n the area involved. E d u c a t i o n a l Technology Center P r o j e c t s I w i l l describe i n t h i s s e c t i o n three p r o j e c t s i n computer based l e a r n i n g from the E d u c a t i o n a l Technology Center. The f i r s t used a timesharing system; the o t h e r s , more recent, were developed d i r e c t l y on personal c o m p t e r s . The  f i r s t p r o j e c t i s a beginning quarter of a c o l l e g e based  4  p h y s i c s course for s c i e n c e - e n g i n e e r i n g majors. The key computer m a t e r i a l s are the o n - l i n e t e s t s , taken at a computer d i s p l a y . Other computer learning m a t e r i a l s are a l s o a v a i l a b l e . The t e s t s have i n them a large amount of l e a r n i n g m a t e r i a l . As soon as a student i s i n d i f f i c u l t y , he or she i s g i v e n a i d which i s s p e c i f i c a l l y r e l a t e d to the d i f f i c u l t y . Each t e s t i s unique. P a s s i n g i s at the competency l e v e l ; students e i t h e r demonstrate t h a t they know the m a t e r i a l or are asked to study f u r t h e r and then take another v a r i a n t of the t e s t . In 10 weeks we give about 15,000 i n d i v i d u a l t e s t s to 400 students. The computer keeps the f u l l class records. The N a t i o n a l Science Foundation provided support. 4  The second p r o j e c t i s concerned with s c i e n t i f i c l i t e r a c y . It hopes t o acquaint students with some fundamental notions about s c i e n c e : What i_s a s c i e n t i f i c theory or model? How i s such a theory discovered? How do we use i t to make p r e d i c t i o n s ? What determines i f i t i s a good theory or a bad theory? The m a t e r i a l , c u r r e n t l y s i x two-hour u n i t s , i s designed f o r a general.audience, w i t h i n i t i a l t e s t i n g done e x t e n s i v e l y i n the p u b l i c l i b r a r y . The m a t e r i a l s have a l s o been tested i n j u n i o r high schools, high s c h o o l s , community c o l l e g e s , and u n i v e r s i t i e s . Support was from the Fund f o r the Improvement of Postsecondary Education.5 The t h i r d p r o j e c t aims at h e l p i n g students become formal o p e r a t i o n a l i n the Piaget sense. The primary l e v e l i s j u n i o r high school. The format for these u n i t s i s s i m i l a r to that f o r the s c i e n c e l i t e r a c y m a t e r i a l s . The p r o j e c t i s supported by the N a t i o n a l Science Foundation.^ Computer Advantages Given a b r i e f view of s e v e r a l a c t i v i t i e s i n v o l v i n g the computer i n l e a r n i n g , we can now say why the computer i s such a p o w e r f u l l e a r n i n g device. At l e a s t two f a c t o r s are c r i t i c a l i n c o n s i d e r i n g the e f f e c t i v e n e s s of the computer i n a i d i n g l e a r n i n g , the'"'TnT'er-a^EitwB^ and *; i *-y to^^d-£gT3Wfti,i ?.a fchp 1 paf»ainq ftyrp&fi*aasa»»tefiT«fe^^ hfl  1  One o f the major problems i n education, p a r t i c u l a r l y e d u c a t i o n which must deal with very l a r g e numbers of students, i s the f a c t that we have l o s t one of the most v a l u a b l e components i n e a r l i e r education, the p o s s i b i l i t y of having l e a r n e r s who are always p l a y i n g an active r o l e i n the l e a r n i n g process. In c l a s s i c a l Greece, with the S o c r a t i c approach to l e a r n i n g , two or t h r e e students worked c l o s e l y with S o c r a t e s , answering Socrates' q u e s t i o n s and therefore behaving as a c t i v e l e a r n e r s . The process was h i g h l y labor i n t e n s i v e . As we had more and more people to educate i t became l e s s and l e s s p o s s i b l e to behave i n t h i s way. We cannot a f f o r d or produce enough master teachers to base our educational system on the S o c r a t i c approach. But we can develop good computer based l e a r n i n g m a t e r i a l i n which the  5  student Is always a c t i v e . The computer may enable us to get back to a much more humanistic, a much more f r i e n d l y , e d u c a t i o n a l system by making a l l of our learners p a r t i c i p a n t s r a t h e r than the spectators they f r e q u e n t l y are i n our present book- and l e c t u r e l e a r n i n g environments. The second advantage o f f e r e d by the computer i s i n d i v i d u a l i z a t i o n o f the process of l e a r n i n g . Everyone says that students are d i f f e r e n t , that each student i s unique, t h a t each student l e a r n s i n d i f f e r e n t ways. But most o f our standard l e a r n i n g procedures, such as the l e c t u r e , are v e r y weak i n allowing f o r these i n d i v i d u a l d i f f e r e n c e s . They t y p i c a l l y t r e a t most students i n the same way. For example, i f a student i n a p a r t i c u l a r p o i n t i n a course l e c t u r e i s l a c k i n g some important background i n f o r m a t i o n , that student i s swept along i n our t r a d i t i o n a l courses with everyone e l s e i n the c l a s s . The missing information i s hard to acquire under those circumstances. The r a t i o n a l procedure would be to allow the student needing s p e c i a l h e l p to stop the major flow of l e a r n i n g at that p o i n t and to go back and p i c k up the background information. But most of our present s t r u c t u r e s f o r l e a r n i n g have no adequate p r o v i s i o n s f o r such a p o s s i b i l i t y . The a c t u a l needs vary between what can be learned i n a few minutes and what can be learned i n a whole course. With the computer the s i t u a t i o n i s e n t i r e l y d i f f e r e n t . Each student can move a t a pace best for that student. Each student w i l l be responding f r e q u e n t l y to questions. (We have found i n our recent programs that a student responds about every f i f t e e n seconds). So the computer, with curriculum m a t e r i a l prepared by e x c e l l e n t teachers, can determine what the student understands or does not understand a t a given p o i n t . Remedial a i d can be given where a p p r o p r i a t e , simply as part of the flow of the m a t e r i a l with no break from the student point of view. Indeed, the student, using well-prepared computer based l e a r n i n g m a t e r i a l , does not have the impression that any " s p e c i a l " treatment i s t a k i n g p l a c e , so no p s y c h o l o g i c a l stigma i s attached to such a i d . With the i n d i v i d u a l i z a t i o n p o s s i b l e with computers, one can hope to achieve the goal of mastery l e a r n i n g , where everyone l e a r n s a l l m a t e r i a l essentially perfectly. So much f o r "why" computers are going to become the dominant e d u c a t i o n a l d e l i v e r y system. The two f a c t o r s mentioned, the unpleasant s i t u a t i o n i n education today and the u s e f u l n e s s of the computer as a way of l e a r n i n g p a r t i c u l a r l y i n d e a l i n g w i t h l a r g e numbers o f students, suggest to me that the computer w i l l move r a p i d l y forward i n education. But we s t i l l must look a t the other s i d e of the q u e s t i o n , the "how" of the development. That i s , how do we move from our present s i t u a t i o n , where computers are l i t t l e used i n l e a r n i n g , to a s i t u a t i o n i n which they are the dominant d e l i v e r y system? T h i s i s the subject of the next s e c t i o n . HOW  WILL WE MOVE TO MUCH GREATER COMPUTER USE?  6  157  Let me f i r s t r e c a p i t u l a t e e a r l i e r information. The p e r i o d ahead i n education, for at l e a s t ten years and probably l o n g e r , i s l i k e l y to be one of tremendous t u r m o i l and s t r i f e . We are j u s t beginning to see the o u t l i n e s of that s t r i f e at the present time. The s t r i f e w i l l be increased g r e a t l y i n education as we begin to move toward such ideas as voucher systems and more d e t a i l e d a c c o u n t a b i l i t y . The t r a d i t i o n a l methods of preserving the s t a t u s quo i n education, or allowing o n l y small incremental changes to take p l a c e , such as the power o f the a d m i n i s t r a t o r s and the unions, w i l l have r e l a t i v e l y l i t t l e e f f e c t ; much of the t u r m o i l i n s c h o o l s w i l l be imposed from the general community. Often changes w i l l be generated by f i n a n c i a l d e c i s i o n s which lead to l e s s money to the schools. The challenge w i l l be the most s e r i o u s one that has been seen i n a very long time i n the educational system. The f o l l o w i n g comment by Peter Drucker gives a view o f s i t u a t i o n from outside academia:  the  "In the next ten or f i f t e e n years we w i l l almost c e r t a i n l y see strong pressures to make schools responsible f o r t h i n k i n g through what kind of l e a r n i n g methods are a p p r o p r i a t e f o r each c h i l d . We w i l l almost c e r t a i n l y see tremendous pressure, from parents and students a l i k e , for r e s u l t - f o c u s e d education and for a c c o u n t a b i l i t y i n meeting o b j e c t i v e s s e t f o r i n d i v i d u a l students. The continuing p r o f e s s i o n a l education of h i g h l y educated raid-career adults w i l l become a t h i r d t i e r i n a d d i t i o n to undergraduate and p r o f e s s i o n a l or graduate work. Above a l l , a t t e n t i o n w i l l s h i f t back to schools and education as the c e n t r a l c a p i t a l investment and i n f r a s t r u c t u r e of a 'knowledge s o c i e t y ' . " 7  Thus, we w i l l have a s o c i e t y more and more unhappy with the c u r r e n t educational system, a s o c i e t y groping for new ways to handle education. Few " s o l u t i o n s " to the problem w i l l be apparent. Home Computers During the same period of time computers, p a r t i c u l a r l y personal computers, w i l l be decreasing i n c o s t , i n c r e a s i n g i n c a p a b i l i t i e s , or (more l i k e l y ) some mixture of these two trends. The changes w i l l often be d r a s t i c . While the term one hears i n the computer industry, TTTTCTTfiTI'nflrrivrTiif^r i s i n t e n t i o n a l l y something of an exaggeration, i t does r e f l e c t what i s happening i n many areas of computer technology. One aspect of the r a p i d development of personal computers that w i l l be extremely important f o r the future of education w i l l be the i n c r e a s i n g presence of the computer i n homes. Homes w i l l represent the l a r g e s t p o s s i b l e market for personal computers, s i n c e i n no other s i t u a t i o n can one speak of m i l l i o n s of u n i t s . There are approximately eighty m i l l i o n American homes; so the number of computers which can be s o l d for home use, provided the o r d i n a r y person can be convinced that the computer i s v a l u a b l e to  7  152  own, i s enormous. The home w i l l be the d r i v i n g force f o r education too, since the commercial pressures for home s a l e s be very great.  will  In a sense, education i s never " f i r s t " with computers. For many years we piggybacked on e s s e n t i a l l y a business or s c i e n t i f i c technology i n computers with education only a poor f o l l o w e r . The new s i t u a t i o n w i l l be s i m i l a r , but with the home market the dominant one. To s e l l computers f o r the home, i t w i l l be necessary that they do something. The average home owner i s not going to buy a computer on the grounds that they are c u r r e n t l y being s o l d to homes, p r i m a r i l y for h o b b y i s t s . The home user of equipment buys an appliance, a device such as a r e f r i g e r a t o r or stove that accomplishes some task or tasks. They don't buy a gadget that they can put together i n various ways to accomplish d i f f e r e n t types of tasksl The s i z e of the home market w i l l depend on the s k i l l o f vendors i n convincing people that the computer i n the home w i l l be u s e f u l to the average person. Some estimates have suggested s i x t y m i l l i o n computers i n homes i n ten years. I do not wish to imply that a s i n g l e a p p l i a n c e - l i k e use o f the computer w i l l d r i v e the home market. On the c o n t r a r y , a v a r i e t y of such uses are l i k e l y to be important. Home word p r o c e s s i n g , f o r example, w i l l be an e x t r e n e l y important use. Home f i n a n c i a l systems, complete enough t o keep a l l the f i n a n c i a l records and write the income tax when asked to, and to a i d i n home f i n a n c i a l d e c i s i o n s , w i l l a l s o be of importance. Personal r e c o r d keeping systems, i n c l u d i n g c l a s s notes, l i s t s , and s i m i l a r uses, are also l i k e l y to be o f major use i n the home. F i n a l l y , e d u c a t i o n a l m a t e r i a l w i l l be one of the types of m a t e r i a l that without question w i l l d r i v e the home market. The s i z e of t h i s market w i l l depend on the q u a l i t y and q u a n t i t y of such a p p l i a n c e l i k e programs. Thus, we w i l l f i n d l e a r n i n g m a t e r i a l based on the computer being developed for home computers, i n some cases almost independently of whether i t w i l l a l s o be usable i n elementary and secondary schools, u n i v e r s i t y , or other l e a r n i n g environments. Schools w i l l use the m a t e r i a l developed p r i m a r i l y for education i n the home even though i t may not be i d e a l l y s u i t e d . I t may be that t h i s m a t e r i a l w i l l often have more c a r e f u l thought put i n t o i t than some of the e a r l i e r products developed p a r t i c u l a r l y f o r the school environment, simply because the p o t e n t i a l market i s so much l a r g e r and users more d i s c r i m i n a t i n g . Schools are already desperately searching f o r computer based l e a r n i n g m a t e r i a l and are f i n d i n g that l i t t l e good m a t e r i a l i s a v a i l a b l e . The people who are using the new l e a r n i n g m a t e r i a l s i n the home w i l l be coming to cur schools and u n i v e r s i t i e s . ThioyiiMiill  aHCTwynatey^^  i f the  educational i n s t i t u t i o n s wish to s u r v i v e , they w i l l provide i t .  8  I S3 I an taking what may seem to many of you, given the nature of the audience, to be a very market-oriented p o i n t of view. But we aust be r e a l i s t i c i n t r y i n g to p l o t the f u t u r e . We must understand that the most fundamental issues that w i l l determine the future are these marketing i s s u e s , not the academic i s s u e s which may be at the f o r e f r o n t of our own minds. Companies When we look at the s c h o o l market, we see i n t e r e s t i n g commercial pressures. The dominant s e l l e r s of e d u c a t i o n a l m a t e r i a l s to schools today are the commercial textbook p u b l i s h e r s . Yet commercial textbook p u b l i s h i n g i s a s t a t i c domain at almost a l l l e v e l s of p u b l i s h i n g . That i s , i t i s d i f f i c u l t f o r a company t o make much progress there, i n the sense of i n c r e a s i n g p r o f i t s . Education i t s e l f i s g e t t i n g d e c l i n i n g amounts o f money. There w i l l be d e c l i n i n g numbers of students f o r many years. The competition between companies i s f i e r c e . To end up with a much l a r g e r share of that market at the present time, considered p u r e l y as a textbook market, i s extremely d i f f i c u l t . So i t i s not s u r p r i s i n g that many of the most i n f l u e n t i a l textbook publishers are now beginning to devote s i z a b l e amounts of e f f o r t , a t t e n t i o n , and money to computer based l e a r n i n g . They see t h i s as a new market, where i t i s not at a l l c l e a r at present who w i l l become dominant. Thus, a minor textbook p u b l i s h e r c o u l d see the p o s s i b i l i t y of becoming a major computer based l e a r n i n g p u b l i s h e r , or a major p u b l i s h e r could see that computer based m a t e r i a l s would very much increase revenues. Or a new company could see t h i s as a p a r t i c u l a r opportunity f o r advancement, allowing them to leap over the e s t a b l i s h e d companies. A l l these s i t u a t i o n s are happening now. The l i s t of textbook p u b l i s h e r s p u t t i n g s i z a b l e resources i n t o computer based l e a r n i n g i s a d i s t i n g u i s h e d one. I t includes such names as John Wiley, Harper & Row, Scott Foresman, Science Research Associates, McGraw-Hill, Random House, Encyclopedia B r i t t a n i c a , and many others. The type of involvement i s d i f f e r e n t i n d i f f e r e n t c o m p a n i e s — t h i s i s , a f t e r a l l , a new market, one that i s poorly understood by everyone. The degree of involvement a l s o d i f f e r s from company to company and i s l i k e l y to d i f f e r i n time. In a d d i t i o n to these e s t a b l i s h e d companies, new companies, o f t e n p a r t i c u l a r l y devoted to e i t h e r e d u c a t i o n a l software or to personal computer software more g e n e r a l l y , are coming i n t o existence. Sizable amounts of venture c a p i t a l are a v a i l a b l e for such companies. These companies, o l d and new, w i l l be s e l l i n g t h e i r wares, and so more and more school d i s t r i c t s and u n i v e r s i t i e s w i l l be able to e a s i l y acquire computer based l e a r n i n g materials. Both o l d and new companies w i l l have people a c t i v e l y s o l i c i t i n g school business. The o l d e r textbook companies may want to t i e i n the computer m a t e r i a l with t h e i r e x i s t i n g textbooks, but  9  the newer companies w i l l have no need f o r t h i s , and so may be open to more adventuresome a c t i v i t i e s . Some of the companies w i l l be s e l l i n g to a combination o f the home and school market. In g e n e r a l the materials developed f o r the home market w i l l be a v a i l a b l e i n the school market t o o . Schools Given the turmoil and f i n a n c i a l r e s t r a i n t s i n the s c h o o l s , the commercial pressures, the p r e s s u r e s c r e a t e d by the home market, and the i n c r e a s i n g e f f e c t i v e n e s s o f the computer as a l e a r n i n g d e v i c e , more and more s c h o o l s w i l l t u r n to computers f o r d e l i v e r y o f l e a r n i n g m a t e r i a l . Indeed, we can already spot t h i s happening, although i n a minor way. One i n t e r e s t i n g sign i s the f a c t t h a t many schools, p a r t i c u l a r l y small schools, no longer have adequately prepared teachers to teach many o f the important courses i n the c u r r i c u l u m . Thus i f we look at high school courses such as trigonometry, advanced mathematics, and science c o u r s e s , r u r a l schools i n the U n i t e d S t a t e s presently are o f t e n not p r o v i d i n g these c a p a b i l i t i e s , at l e a s t not i n a way t h a t i s competitive with the b e t t e r l a r g e urban schools. Computers w i l l be a mechanism f o r e q u a l i z i n g opportunity f o r students by p r o v i d i n g computer based l e a r n i n g courses i n these d e c l i n i n g areas, courses that otherwise would n o t be a v a i l a b l e . H o p e f u l l y , these courses w i l l be developed by the best i n d i v i d u a l s from a l l over the country. We may see a decreased r o l e o f the formal school and the f o r m a l u n i v e r s i t y i n our e d u c a t i o n a l system. Much education w i l l be able to take place i n the home i n a f l e x i b l e f a s h i o n . At the u n i v e r s i t y l e v e l we already see one outstanding example of a development o f t h i s kind, The Open U n i v e r s i t y i n the United Kingdom, but s t i l l with r e l a t i v e l y l i t t l e use o f computers. The Open U n i v e r s i t y has demonstrated t h a t good c u r r i c u l u m m a t e r i a l i n home environments can be e f f e c t i v e as a l e a r n i n g mode and economical as compared with the standard c o s t o f education. Voucher systems, i f they are enacted, w i l l make home l e a r n i n g much more l i k e l y . I do not wish to imply that a l l education w i l l move to the home. Indeed, a view of the e d u c a t i o n a l system such as that shown i n George Leonard's book, Education and E c s t a s y , suggests that the s o c i o l o g i c a l components, the f a c t o r s a s s o c i a t e d with l i v i n g with o t h e r people and l i v i n g with o n e s e l f , w i l l s t i l l probably best take p l a c e i n small group environments w i t h i n schools. But many o f the knowledge-based components o f l e a r n i n g may move to the home. Types o f Usage We have discussed very l i t t l e about the way computers w i l l be used w i t h i n the school system. Something needs to be said about t h i s , i f o n l y to counteract some o f the c u r r e n t propaganda.  I wish to go on record as s t a t i n g that the computer w i l l be used i n a very wide v a r i e t y of ways w i t h i n our e d u c a t i o n a l system. The n o t i o n that some " r i g h t " way e x i s t s to use the computer, and that other modes of computer usage are somehow wrong, i s one that has been promulgated, I am a f r a i d , by a number of i n d i v i d u a l s and groups i n recent years. Indeed, o f t e n staged debates a t meetings comparing types o f usage have been h e l d , with the i m p l i c a t i o n that there are r i g h t and wrong ways to use the computer i n education. Books have been organized i n such a way that i t sounds as though there were a competition f o r d i f f e r e n t types o f computer usage. These debates, o f t e n on p h i l o s o p h i c a l grounds, have made a t a c i t assumption that a r i g h t way to use the computer e x i s t s , i f o n l y t h a t way c o u l d be discovered. Mostly the authors have had a naive b e l i e f i n t h e i r " r i g h t " way, and then s e t out to t r y to e s t a b l i s h a case f o r t h e i r b e l i e f s . The p r i n c i p a l problem with t h i s type o f reasoning i s that i t o f t e n does not proceed from i n s t r u c t i o n a l bases, nor does i t proceed from e m p i r i c a l bases, experimental s t u d i e s . That i s , the issues t h a t dominate are o f t e n t e c h n o l o g i c a l i s s u e s , the nature o f the computer hardware and what can be done with the computer hardware. These w r i t e r s are t r y i n g to carve some unique niche f o r the computer among other l e a r n i n g media. These t a e h n o l o g i c a l l y - b a s e d and media-based arguments f o r a s i n g l e type o f computer usage are, I b e l i e v e , e n t i r e l y m i s l e a d i n g . The d e c i s i o n s as to how to use c o m p u t e r s — t h e modes o f computer usage, the a r e a s — s h o u l d be made e n t i r e l y on pedagogical grounds, the questions o f what aids l e a r n e r s r a t h e r than on these p h i l o s o p h i c a l , media, or t e c h n o l o g i c a l grounds. Whenever d e c i s i o n s are made on pedagogical grounds, i t w i l l be found that a wide v a r i e t y of computer uses w i l l be employed, uses which are o f t e n adapted to the i n d i v i d u a l s i t u a t i o n being considered. There i s no s i n g l e " r i g h t " way to use computers, but rather a great v a r i e t y o f ways. I w i l l give a b r i e f c l a s s i f i c a t i o n o f the various ways the computer can be used. This l i s t i s not exhaustive nor does i t show f i n e d e t a i l . But i t may be u s e f u l to a t l e a s t consider the range. Computer L i t e r a c y . Computer l i t e r a c y i s i l l - d e f i n e d and so much debated. I t i s recognized that a t a l l l e v e l s of education, s t a r t i n g perhaps as e a r l y as eight or nine y e a r s o l d and c o n t i n u i n g through the school system, u n i v e r s i t y , and a d u l t education, that i n d i v i d u a l s i n our s o c i e t y need t o understand the v a r i o u s ways the computer i s going to be used i n that s o c i e t y ; they need to understand the p o s i t i v e and negative consequences of those ways. Few f u l l - s c a l e computer l i t e r a c y courses e x i s t . Indeed, what o f t e n passes as computer l i t e r a c y i s vague h i s t o r y or l e a r n i n g t o program i n a s i m p l i f i e d way, to be d i s c u s s e d i n a moment. So t h i s i s s t i l l very much an open area f o r computer uses. S p e c i a l i z e d courses are needed f o r each group addressed; thus, computer l i t e r a c y f o r teachers i s a p r e s s i n g n a t i o n a l i s s u e .  1<*Z  A l l these courses need to consider such important future uses as word processing, personal f i n a n c i a l and record keeping systems, and educational m a t e r i a l . Learning to Program. Learning to program i s a l r e a d y a r a p i d l y i n c r e a s i n g a c t i v i t y i n our u n i v e r s i t i e s and s c h o o l s . I t represents i n grade s i x through twelve the most common usage of computers at the present time. Unfortunately, where i t happens at t h i s l e v e l i t i s o f t e n a d i s a s t e r , harming more than h e l p i n g the student. The major problem i s the way programming i s taught. A whole group of people i s being taught a set of techniques which are no longer adequate to the programming a r t today. These techniques were common i n the e a r l y days of computing, but they are inadequate according to today's standards. Many of the people l e a r n i n g to program i n j u n i o r high school and high s c h o o l cannot overcome the i n i t i a l bad h a b i t s which have o f t e n been i n s t i l l e d i n them when they come to the u n i v e r s i t i e s . Many u n i v e r s i t i e s are now r e p o r t i n g t h i s phenomenon. The main c u l p r i t i s BASIC. I t i s not that BASIC has to be taught i n a way that i s a n t i t h e t i c a l to everything we know about programming today. But i t almost i n e v i t a b l y _is_ taught i n such a 2&Sd&£S£23ttt&t^^ Indeed, fashion. the analogy i s c l o s e i n that junk food tends to destroy the body's d e s i r e for better types of food. But the analogy i s weak i n one regard: BASIC i s the i n i t i a l language of the vast m a j o r i t y of these people. I t i s as i f you started feeding junk food to babies one day o l d and d i d n ' t g i v e them anything e l s e u n t i l they were six] I f I could leave you with one raessace, perhaos the roost p r e s s i n g message  The f o l l o w i n g recent comment by a d i s n t i n g u i s h e d computer s c i e n t i s t , ?fiftg3SraEffip^^, i s relevant:  JenLs "LrfJ  What programming languages should we teach? There are a number of p o s s i b i l i t i e s f o r junior high and high s c h o o l s . "tJiigjGj^is c e r t a i n l y one i n t e r e s t i n g p o s s i b i l i t y , although I must confess that some features of Logo are d i f f e r e n t from those recommended i n the best modern programming p r a c t i c e s . Logo, however, i s introduced i n a problem s o l v i n g environment, and that i s very much to i t s advantage. Often i t s main i n t e n t i s presented not to teach programming but to teach more general problem s o l v i n g c a p a b i l i t i e s  11.3 or some s p e c i f i c area of mathematics. But i t s general problem s o l v i n g e f f e c t i v e n e s s has yet to be demonstrated i n our mass s c h o o l environments with o r d i n a r y teachers. Another good p o s s i b i l i t y i s ^ g g g j ^ o r , ^ ^ § f ^ ^ ^ ^ ^ ^ S n H t l ^ . The m a t e r i a l developed at the U n i v e r s i t y of Tennessee and s o l d by McGraw-Hill under the name of "Computer Power" i s an e x c e l l e n t example of an approach of t h i s k i n d . I f one looks f o r p r i n t m a t e r i a l that i s usable at the high school and perhaps even a t the j u n i o r high school l e v e l at the present moment, the "Computer Power" m a t e r i a l looks to me to be e a s i l y one of the best possibilities. Another approach i s to develop some i n t e r e s t i n g c a p a b i l i t y based on a ' a g ^ ^ S ^ i i ^ i ^ p g j g g ^ ^ ^ ^ ^ K ^ c a g f f For example, the recent K a r e l , The Robot from Wiley f o l l o w s such an approach. T u r t l e geometry, i n Logo, i s the best known example. Learning Within Subject Areas. Undoubtedly the l a r g e s t use of the computer i n schools at a i l l e v e l s w i l l e v e n t u a l l y be not the c a t e g o r i e s j u s t discussed but r a t h e r the use of the computer as an a i d i n l e a r n i n g mathematics, i n l e a r n i n g to read, i n l e a r n i n g to write, i n l e a r n i n g c a l c u l u s , and i n a l l the o t h e r tasks associated with the l e a r n i n g process. One person may work alone a t a d i s p l a y or s e v e r a l may work together. When one looks a t these l e a r n i n g tasks i n d e t a i l , again o n e " f i n d s a great v a r i e t y of computer use, ranging from t u t o r i a l m a t e r i a l , to i n t u i t i o n b u i l d i n g , to t e s t i n g , to aids i n management o f the c l a s s for the student (feedback on what i s needed and how to go about g e t t i n g i t ) , and the teacher. The three p r o j e c t s presented e a r l i e r show something of the range of p o s s i b i l i t i e s . Unfortunately, much of the m a t e r i a l now a v a i l a b l e of t h i s type i s very p r i m i t i v e . We are, however, r a p i d l y l e a r n i n g to develop better m a t e r i a l to a i d l e a r n i n g . PRODUCTION PROCESS I f we are to move to meet t h i s new f u t u r e , where the computer w i l l be the dominant e d u c a t i o n a l d e l i v e r y system, a c r i t i c a l aspect w i l l be the generation of e f f e c t i v e l e a r n i n g m a t e r i a l . We need new courses and e n t i r e new c u r r i c u l a , spanning the e n t i r e e d u c a t i o n a l system. Hence, the development we are t a l k i n g about i s a n o n t r i v i a l process. I t i s the degree of success of the development process that w i l l t e l l whether we w i l l improve or h u r t education. We must convince the l i k e l y d i s t r i b u t o r s that i t i s important to develop q u a l i t y m a t e r i a l s , not the junk t y p i c a l l y a v a i l a b l e today. The development of c u r r i c u l u m m a t e r i a l i n any f i e l d and with any medium and at any l e v e l i s a d i f f i c u l t process. I t cannot be done by amateurs who are doing i t simply as a spare time a c t i v i t y . Many new observers i n t h i s f i e l d , l o o k i n g at the problems q u i c k l y ,  tend to underrate these problems of developing e f f e c t i v e l e a r n i n g m a t e r i a l . Hence, some of the s o l u t i o n s which have been proposed are s o l u t i o n s which are simply not adequate to the problems. Some of these s o l u t i o n s assume only small incremental changes i n the c u r r i c u l u m s t r u c t u r e and do not understand the magnitude of the development neceszry. We cannot d i s c u s s f u l l y i n t h i s paper a l l the aspects of the production process. The E d u c a t i o n a l Technology Center has extensive l i t e r a t u r e a v a i l a b l e concerning these i s s u e s f o r those interested. . S e v e r a l c r i t i c a l p o i n t s concerning products should be made to g i v e the reader a reasonable o v e r a l l viewpoint. The p r o d u c t i o n system i s a complex system, one that should i n v o l v e many types of people with many d i f f e r e n t s k i l l s . I f one looks a t the p r o d u c t i o n o f any educational m a t e r i a l , one sees that that i s the case. We can l e a r n much by examining e f f e c t i v e c u r r i c u l u m production systems, such as that c u r r e n t l y i n use i n The Open U n i v e r s i t y , that used i n producing the major c u r r i c u l u m e f f o r t s i n the United S t a t e s more than ten years ago, and that i n v o l v e d i n such areas as the development of textbooks. What we need to r e s i s t i s the notion that one person, perhaps a teacher i n h i s or her spare time, w i l l do i t a l l . I do not b e l i e v e that any s i z a b l e amount of good c u r r i c u l u m m a t e r i a l w i l l be produced by t h i s method. Furthermore, I do not b e l i e v e that the devices which are being urged f o r these teachers, such as simple-minded authoring systems based on toy languages ( P i l o t ) w i l l be e f f e c t i v e . Nor do I think that languages such as Tutor w i l l be e f f e c t i v e , because they do not meet the reasonable c r i t e r i a associated with modern programming languages. Most of these languages are o l d i n t h e i r design, and few of them understand the nature of s t r u c t u r e d programming. A s e r i o u s p r o f e s s i o n a l approach i s needed i f we are to maintain the q u a l i t y o f the computer based l e a r n i n g m a t e r i a l s produced. We can see a number of stages needed i n such a p r o f e s s i o n a l approach, l i s t e d below. a) b) c) d) e) f) g) h) 1) j) k) The  Preplanning E s t a b l i s h i n g g o a l s , o b j e c t i v e s , and rough o u t l i n e s S p e c i f y i n g the m a t e r i a l s p e d a g o g i c a l l y Reviewing and r e v i s i n g t h i s s p e c i f i c a t i o n Designing the s p a t i a l and temporal appearance of the m a t e r i a l Designing the code Coding T e s t i n g in-house Revising F i e l d testing Revising l a s t two stages may  be repeated twice.  In the e n t i r e process the e d u c a t i o n a l i s s u e s , as opposed to the t e c h n i c a l issues, should be dominant. The best teachers and i n s t r u c t i o n a l designers should be involved i n stages c and d to assure the q u a l i t y of the product. PRESENT STEPS T h i s paper has presented an overview o f some of the problems a s s o c i a t e d with reforming an e n t i r e e d u c a t i o n a l system during the next twenty years. Many d e t a i l s are e i t h e r not mentioned or t r e a t e d very h a s t i l y . But I hope I have g i v e n enough d e t a i l s to convince you o f the main d i r e c t i o n s that need to be taken. As teachers, most o f you are undoubtedly i n t e r e s t e d i n what you should do now to work toward a more e f f e c t i v e f u t u r e f o r e d u c a t i o n . F i r s t , you must decide whether you would l i k e to be i n v o l v e d i n the type of curriculum development I suggested w i l l be necessary. I f you do want to be i n v o l v e d , you must take a longrange view o f how to prepare f o r t h i s a c t i v i t y . I would not advise you to buy an Apple and s t a r t to use i t l Nor, as you might suspect, would I advise you to take courses i n BASIC. But i t would be d e s i r a b l e to take a v a r i e t y of courses, i f they a r e a c c e s s i b l e to you or to study on your own, i n c e r t a i n areas. Here are some suggestions. The f i r s t three r e f e r to areas of l e a r n i n g , e i t h e r through formal courses or through i n f o r m a l methods. 1. Learning theory. Good c u r r i c u l u m development cannot be developed without some a p p r e c i a t i o n o f how people l e a r n , even though there i s no s i n g l e coherent theory there. Courses i n l e a r n i n g theory may help, based on the research l i t e r a t u r e concerning l e a r n i n g . 2. Curriculum development. The question of how to develop good c u r r i c u l u m m a t e r i a l i s one that deserves serious study. Some u n i v e r s i t i e s provide such courses. Some textbooks e x i s t . Many o f the i s s u e s are independent of computers, r e f e r r i n g to developing with any l e a r n i n g media. 3. Modern programming languages. You might want to become acquainted with modern programming languages, such as ^ ^ s ^ ^ » and *3§t> Again, you must be c a r e f u l here. I t i s p o s s i b l e to meet these languages e i t h e r i n an o l d fashioned environment or i n one that s t r e s s e s structured programming. You want the second possibility. Look at the textbook. I f i t doesn't introduce procedures u n t i l a t h i r d o f the way or even f u r t h e r along, don't take the course. This i s n ' t the o n l y f a c t o r , but i t i s a good way of d i s t i n g u i s h i n g reasonable from unreasonable courses. Avoid the "CAI" l a n g u a g e s — t h e y are inadequate, not s u i t a b l e f o r s e r i o u s m a t e r i a l . Look at the authoring approaches based on  15  modern s t r u c t u r e d  languages.  4. L i s t e n to students. In your own teaching, begin to move away from the l e c t u r e mode p r e s e n t a t i o n i n t o a more S o c r a t i c mode. A c r i t i c a l f a c t o r i s l i s t e n i n g to what students say and watching what they do. T h i s means that when you ask q u e s t i o n s , you have to wait f o r answers! I t a l s o means working more i n d i v i d u a l l y with students i n groups of two to four. I t i s o n l y by t h i s procedure t h a t you w i l l begin to b u i l d up the i n s i g h t s you need f o r how students a c t u a l l y behave when they are l e a r n i n g . People whose primary mode of i n t e r a c t i o n with students i s through the l e c t u r e mode or through textbooks are seldom the best c h o i c e s f o r p r e p a r i n g computer based l e a r n i n g m a t e r i a l . The development o f computer based l e a r n i n g m a t e r i a l w i l l need v a s t numbers of experienced teachers, teachers who have been l i s t e n i n g to t h e i r students and who understand student l e a r n i n g problems. 5. P e r s o n a l computers. Begin to use a v a r i e t y of personal computers, with p a r t i c u l a r emphasis on the new generation of 16 b i t machines. Read the j o u r n a l s that t e l l you about new equipment. Watch f o r voice input, b e t t e r g r a p h i c s , and f u l l multimedia c a p a b i l i t i e s . 6. C r i t i c a l a t t i t u d e . Look a t a good b i t of computer based l e a r n i n g m a t e r i a l , t r y i n g to develop a c r i t i c a l a t t i t u d e toward it. Don't be overwhelmed simply because i t i s i n t e r a c t i v e or because the computer i s involved. Keep your mind on the l e a r n i n g i s s u e s and l e a r n to develop some s e n s i t i v i t y as to what e x i s t i n g m a t e r i a l helps l e a r n i n g and what doesn't. Most e x i s t i n g m a t e r i a l i s poor. j o u r n a l s that s p e c i a l i z e i n c r i t i c a l  Pind out why. reviews.  Read the  7. Work with others. The development of good computer based l e a r n i n g m a t e r i a l i s best done i n a group. Work with others i n d i s c u s s i n g g o a l s , s t r a t e g y , and the d e t a i l s o f design. 8. Future o r i e n t a t i o n . Concentrate on the long-range s i t u a t i o n , not today or tomorrow. D e c i s i o n s which are "good" from a short-range p o i n t of view may be u n d e s i r a b l e i n the long range to both you and to the future of our e n t i r e e d u c a t i o n a l system. So keep the long-range point of view s t r o n g l y i n mind. 9. V i s i o n s . Begin to think about what type of f u t u r e e d u c a t i o n a l system would be both d e s i r a b l e and p o s s i b l e . I f you want to i n f l u e n c e the f u t u r e , you must have v i s i o n s . •Developing q u a l i t y computer-assisted i n s t r u c t i o n demands forethought; those of you who are u n f o r t u n a t e l y caught up i n expedient movements i n education need to take a c l o s e r , more courageous look at the nature of the hope on Pandora's c h i p . You're d e a l i n g with as powerful a t o o l as the gods have ever given us."^  lfc7  References: 1.  Servan-Schreiber, Jean-Jacques, The World Challenge. York: Simon and Schuster from The M i t s u b i s h i Report.  2.  Staky h. r t i e w ^ M ' wi th Cw...pul&j»a.  3.  Boyer, E., quoted i n "Report on Educational Research," February 3, 1982.  4.  Bork, A., "Computer-Based I n s t r u c t i o n i n P h y s i c s . " Today, 34, 9, (September 1981).  5.  Bork, A., Kurtz, B., F r a n k l i n , S., Von Blum, R., Trowbridge, D., "Science L i t e r a c y i n the P u b l i c L i b r a r y . " Paper, A s s o c i a t i o n o f E d u c a t i o n a l Data Systems, Orlando, February 1982.  New  D l l l B i i i a , Masii'aihusefciai*  Von Blum. R., "Computers i n Informal Learning: Study," November 1980.  Physics  A Case  Arons, A., Bork, A., C o l l e a , F., P r a n k l i n , S., and K u r t z , •Science L i t e r a c y i n the P u b l i c L i b r a r y - B a t t e r i e s and Bulbs." Paper, Proceedings of the N a t i o n a l E d u c a t i o n a l Computing Conference, Denton, Texas, June 1981. 6.  B.,  Trowbridge, D. and Bork, A., "A Computer Based D i a l o g f o r Developing Mathematical Reasoning of Young A d o l e s c e n t s . " Paper, Proceedings of the National Educational Computing Conference, Denton, Texas, June 1981. Trowbridge, D. and Bork, A., "Computer 3ased Learning Modules for E a r l y Adolescence." Paper, World Conference on Computers i n Education 1981, Lausanne, J u l y 1981.  7.  Drucker, Peter F., The Changing World of the E x e c u t i v e . York: Times Books, 1982.  9.  Quote from Burns, H., "Pandora's Chip: Q u a l i t y CAI," P i p e l i n e , F a i l 1981.  11  Concerns about  New  

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