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Variables predicting achievement in introductory computer science courses Sall, Malkit Singh 1989

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VARIABLES PREDICTING ACHIEVEMENT IN INTRODUCTORY COMPUTER SCIENCE COURSES By MALKIT SINGH SALL B.Sc, The University of British Columbia, 1975 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE STUDIES (Faculty of Education, Department of Mathematics and Science Education—Computing Studies Education) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA April 1989 © Malkit Singh Sail, 1989 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of MAT^EMfiT)CS> / f / I / f l S C / ^ C c ^bUO^TfO The University of British Columbia Vancouver, Canada DE-6 (2/88) ii ABSTRACT Thesis Supervisor: Dr. Marvin Westrom This study examined the problem of predicting achievement for introductory computer science courses at the college or university level. A questionnaire was developed based on 22 predictor variables that were identified in the review of the literature to predict the final course examination score and the final course grade. The predictor variables were grouped into secondary subject areas and other prior characteristics of each student. The total score on the questionnaire, the score on the KSW Test (Konvalina, Stephens and Wileman, 1983) and the 22 variables were correlated with the final course examination scores and the final course grades using the Pearson product-moment correlation coefficient. Five secondary subject areas correlated significantly (p < 0.003) with the final course examination score and the final course grade: prior computer science achievement, secondary English achievement, secondary languages other than English, secondary mathematics achievement and secondary science achievement. Eight other characteristics correlated significantly with the final examination scores: number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. Eleven further characteristics correlated significantly with the final course grades: current age, year/grade level, number of iii years after secondary graduation, number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number cf time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. The factor analysis calculations showed that there were five main factors (each composed of a combination of predictor variables) that were labelled as Computer Literacy, Scholastic Achievement and Language Learning, Mathematical Reasoning and Scholastic Achievement, and Finger Dexterity. The questionnaire developed in this study is as good as the KSW Test and is faster to complete (10 minutes versus 40 minutes). Interpretations of the findings, conclusions, limitations of the study, and implications of the study are discussed. iv TABLE OF CONTENTS A B S T R A C T i i T A B L E OF CONTENTS iv LIST OF TABLES ix LIST OF FIGURES x i A C K N O W L E D G E M E N T x i i CHAPTER 1 INTRODUCTION 1 a General statement of the problem 1 b Statement of the research questions 2 c Definition of terms 4 d Delimitations 4 CHAPTER 2 REVIEW OF T H E LITERATURE 5 a Review of previous research 5 1 University Level 5 2 College Level 12 3 Secondary and College Level 16 4 Secondary Level 16 5 Elementary and Secondary Level 19 V 6 Elementary Level 21 b Summary • 21 CHAPTER 3 M E T H O D 24 a Pilot Study 24 b Description of students: Population and Sample 25 c Research design and procedures 27 d Data Analysis 28 1 Descriptive statistics 2 Inferential statistics e Description of measures employed 30 CHAPTER 4 FINDINGS 35 a Overview of data collection and analysis 35 b Description of findings pertinent to each hypothesis 35 1 Descriptive Statistics 37 2 Inferential Statistics 40 3 Discussion of Research Questions 43 4 Bivariate multiple regression equations 45 5 Multivariate Multiple Regression Equations 47 vi c Omerftodings , 49 CHAPTER 5 SUMMARY AND DISCUSSION 53 a Summary of research problem, method, and findings 53 1 Research Problem 53 2 Method 53 3 Findings 53 4 Other Findings 55 b Interpretations of the Findings 56 1 Secondary Subject Areas 56 2 Characteristics Other Than Secondary Subject Areas 57 3 Discussion of the Descriptive Statistics 62 4 Predictability of Questionnaire Scores Versus KSW Test Scores 63 5 Prediction Equations 64 6 Factor Analysis of All Sections 65 c Conclusions 67 d Limitations of the Study 67 e Implications 70 f Suggestions for further research 71 vii BIBLIOGRAPHY 72 APPENDIX A [PILOT STUDY] 76 Variables Predicting Achievement in Introductory Computer Science Courses 76 APPENDIX B [PILOT STUDY AND MAIN STUDY] 83 Computer Science Aptitude Examination 83 APPENDIX C ANSWER SHEET [PILOT STUDY AND MAIN STUDY] 91 Answer Sheet for Computer Science Aptitude Examination 91 APPENDIX D ANSWER KEY [PILOT STUDY AND MAIN STUDY] 92 Answer Key for Computer Science Aptitude Examination (KSW Test) 92 APPENDIX E INSTRUCTIONS [MAIN STUDY] 93 Directions for Questionnaire and Examination 93 APPENDIX F [MAIN STUDY] 94 Variables Predicting Achievement in Introductory Computer Science Courses 94 APPENDIX G ANSWER SHEET [MAIN STUDY] 101 Answer Sheet For Variables Predicting Achievement In Introductory Computer Science Courses 101 viii APPENDIX H MARKING KEY [PILOT STUDY AND MAIN STUDY] 103 Marking Key For Measure "Variables Predicting Achievement In Introductory Computer Science Courses" 103 APPENDIX I [MAIN STUDY] 105 Pearson Product-Moment Correlation Coefficients Among All Variables 105 APPENDIX J [PILOT STUDY AND MAIN STUDY] 112 Twenty-two Variables Used in The Study 112 LITERATURE INDEX 114 i x LIST OF TABLES TABLE 1 23 Summary by Predictor Variables, Student Levels, and Number of Studies 23 TABLE n 33 Questionnaire variables and indexed studies 33 TABLE III 37 Descriptive Statistics of All 26 Variables 37 TABLE IV 41 Pearson Product-Moment Correlation Coefficients For All Eight Sections 41 TABLE V 44 Fisher's Z-transformations 44 TABLE VI 47 Parameters of the Final Grade Equation 47 TABLE VII 48 Parameters of the Final Examination Equation 48 TABLE VIII 49 Percentage of Variance of the Factors ...49 X TABLE LX 50 Weightings of Each Variable on Each Factor 50 TABLE X 66 New Independent Variables Created by the Factor Analysis 66 TABLE XI 106 Pearson r's Among All 22 Variables 106 x i LIST OF FIGURES Figure 1 39 Distribution Curve For Number of Years After Secondary Graduation 39 Figure 2 52 Frequencies of Significant Pearson Correlation Coefficients 52 x i i ACKNOWLEDGEMENT I want to thank my thesis committee Dr. D. Allison, Dr. S. Donn, Dr. H. Ratzlaff, and Dr. M. Westrom for their guidance and their invaluable recommendations. I also want to thank Dr. W. Boldt for his help in analyzing the results of the Factor Analysis Calculations. I wish to thank all the instructors, professors, and students in the Department of Computer Science and the Department of Computing Studies Education for their willing participation in this study by sacrificing their time and energy. I need to thank my wife Harjindro (Jindy), my children Jessica and Kevan, my dad Kabul, and my mom Gurbachan, my brother Dovinder, my sister Mohinder, my cousins Avtar, Kerpaul, and Kuldeep, my friend Sucha, my father-in-law Gurbux, my mother-in-law Ratni, my sisters-in-law Surinder and Preet, and brother-in-law Lucky for their unfailing support and confidence in my aspirations. Chapter 1/ 1 CHAPTER 1 INTRODUCTION a General statement of the problem One problem in teaching an introductory computer science course is the lack of a measure to predict achievement. Frustration is experienced by some of the students and the instructor when this course is open to a wide range of students with varied backgrounds. Some of the students are frustrated when they exert an effort for the course requirements without achieving a passing score. The instructor is frustrated because some students have difficulty learning concepts. The instructor must teach these concepts over again or find different ways to teach these concepts. Counselling may alleviate this problem, and counselling may be facilitated by having predictive information from some type of measurement instrument. The instrument should ideally be short in length, easy to administer, easy to mark, and be able to give a good indication of a student's aptitude for computer science courses. Based on the results from this instrument, more adequate counselling could be given to students. For example, a different course or direction may be suggested for some students whereas other students would be encouraged to remain in the course (Petersen and Howe, 1979, 184). Computer science course resources are usually limited and expensive and with high demand, high drop out, and high withdrawal, indicate a need for identifying the appropriate students (Konvalina et al, 1983; 106, Sorge and Wark, 1984, 36; and Stephens et al, 1985, 46). By administering a survey instrument or pretest, the instructor may be able to give the student a strong indication of the probability of success. Previous research done in the area of trying to predict achievement in introductory computer science courses has strongly correlated mathematical reasoning ability and prior grade point average with the level of achievement (Alspaugh 1970, 1972; Petersen and Howe 1979; Wileman et al 1981; Wileman et al 1982; Konvalina et al Chapter 1 / 2 1983; Kaiser 1982; Nowaczyk 1983; Surge and Wark 1984; Stephens et al 1985; Lockheed et al 1985; and Oman 1986). Mazlack (1980) concluded that it is very difficult to predict computer science achievement at the college or university level since he found no significant correlation between computer science achievement and previous academic areas (arts and science), gender, semester or year level, and IBM's Programming Aptitude Test scores. The purpose of this study was to develop a measurement instrument that would have a better prediction record and a higher correlation with computer science achievement than the latest existing instrument developed by Konvalina, Stephens, and Wileman (1983) (the KSW Test). Furthermore, this measurement instrument should be quicker to administer, easier to mark, and give a better indication of the student's aptitude for computer programming. This measurement instrument (Appendix A) was developed by combining as many variables as possible that had been identified in the review of the literature. All students that volunteered were administered the survey instrument (Appendix A) and the test (Appendix B) at the beginning or the end of their introductory computer science course class. In the review of the literature, no other study was found that correlated 20 predictor variables with introductory computer science achievement. This makes this study unique. b Statement of the research questions An extensive review of the literature revealed that mathematical reasoning ability and overall grade point average are two common predictors of success in computer science. Grade point average is a global predictor of achievement for all students but is not necessarily a specific predictor for computer science achievement. A high grade point average means that the student is generally intelligent and is capable of high Chapter 1 / 3 achievement in almost any subject area. Mathematical reasoning ability is a precise, clearly defined subject area that has been established as a prerequisite (Wileman et al, 1981 and Petersen and Howe, 1979). This study attempted to establish predictors for a specific area such as computer science achievement. This is why grade point average must be replaced with more specific predictor variable(s). Therefore, the following questions were formulated: 1 Which secondary subject area(s) final course grades correlate significantly with the final course examination scores and/or the final course grades of introductory computer science courses? 2 Which other variables or characteristics (such as number of mathematics courses and expected grade) correlate significantly with the final course examination scores and/or the final course grades in introductory computer science courses? 3 Will the total scores from the newly developed questionnaire correlate higher than the KSW Test scores with the final course examination scores and/or the final course grades in introductory computer science courses? 4 Is it possible to develop effective prediction equations to predict the final course examination scores and the final course grades for introductory computer science courses based on the total scores from the questionnaire and/or the total scores from the KSW Test? Chapter 1 / 4 c Definition of terms BASIC, PASCAL, LOGO, FORTRAN, ASSEMBLY, MACHINE, COBOL, C, LISP, APL, MODULA, PL/I, PL/C, ADA, RPG, ALGOL, SNOBOL, FORTH, PROLOG, NATAL, and SMALLTALK are all names of computer programming languages used to program computers. d Delimitations This study was limited to examining the noncognitive type of predictor variables and using these variables in the development of this measurement instrument. Cognitive variables refer to psychological aspects and perspectives such as self-efficacy, personality, cognitive style, and other psychological factors involved in programming computers. The noncognitive variables described in this study refer to the 22 variables listed in Appendix J that were used in the development of the questionnaire. The dependent variables were the two achievement scores. Achievement was taken as the final course examination score and the final course grade in the introductory computer science course. Both marks were determined by the instructor of each course. Chapter 2/ 5 CHAPTER 2 REVIEW OF THE LITERATURE a. Review of previous research The review of the literature is grouped according to the six educational levels of the students involved in the research (ie. the elementary level, elementary and secondary levels combined, secondary level, secondary and college levels combined, college level, and university level). The researchers are listed chronologically and then alphabetically under each heading. 1 University Level Bauer et al (1968) undertook a study to determine predictors of computer programming success and computer programmers. The dependent variable was the final grade assessed by the instructor. The predictor/independent variables were "the IBM Aptitude Test for Programmer Personnel (ATPP), the Strong Vocational Interest Blank (SVIB), . . . the College Qualification Test (CQT), . . . and grade point averages (GPA's) attained in all previous courses at Michigan State University" (1160-61). The best predictor found by calculating correlation coefficients was GPA. The correlation coefficient between the final course grade and GPA was 0.68 (p < 0.05). Bauer et al (1968) also calculated multiple regression correlations using two or more predictor variables with the final course grade to establish the optimum prediction combination. The two best combinations using GPA were GPA and ATPP Part II (R = 0.76) and GPA, ATPP Part II, and SVIB Computer Programmer (R = 0.81). If GPA is not available, then other best combinations are given where Rvalues range from 0.61 to 0.68. Bauer et al (1968) conclude that a good predictor of computer science success is GPA (r = 0.68 (p <0.05)) but the best predictor is a combination of three predictors GPA, ATPP Part II (figure series), and SVIB Computer Programmer where (R = 0.81). Chapter 2/ 6 A research study done by Alspaugh (1970, 1972) attempted to focus on the required abilities of students to obtain a high level of proficiency in computer programming. The following three tests were adrninistered in the first week of classes: 1 The Thurston Temperament Schedule, 2 LBM Programmer Aptitude Test, and 3 the Watson-Glaser Critical Thinking Appraisal. The scores of SCAT Quantitative and Verbal Test were also used which were acquired from the University of Missouri Testing and Counseling Bureau. The measure of the degree of mathematical background of each student was done by using a "LIKERT' type of scale with a range from zero to six. Alspaugh (1970, 1972) concluded that the mathematical background of each computer programming student appears to be a main influence on the achievement level of both programming languages Basic Assembly Language and FORTRAN IV (correlation coefficient of 0.411 (p < 0.01)). Previous characteristics of each student that had a bearing on the level of programming proficiency in Basic Assembly Language did not appear to change for FORTRAN IV. An investigation study conducted by Buff (1972) explored the variables needed for predicting achievement in FORTRAN programming courses. Measurements in the areas of aptitude, achievement, and socioeconomics were done by using ten independent variables. After analyzing the data for the control and experimental groups, Buff (1972) concluded that the Grade-Point Average variable was the best predictor of the criterion variable for every regression equation that was calculated. Furthermore, combinations of predictor variables were more effective for predicting each group studied than any one single predictor variable (2191). Schroeder (1978) studied the predictors mathematical reasoning ability, spatial reasoning ability, and Piagetian formal thinking ability for predicting achievement in seven computer programming classes at university. A test measuring all three abilities was developed by Schroeder (1978) with reliability confirmed by pilot testing and Chapter 2/ 7 validity established by experts in each of the three areas. The independent variables were the three subtests and the dependent variable was the achievement level of the programming classes. Pearson product-moment correlations were calculated between all independent and dependent variables. The results of the data analysis indicated that the correlation between mathematical reasoning and achievement was the best. Research done by Petersen and Howe (1979) explored the relationship between selected predictors and academic performance (course grades) in introductory computer science courses. Three sources of data were: 1 A survey questionnaire of 15 items on biographical data that covered areas such as sex, siblings, age. High school and college GPA's, number of mathematics and science courses taken in high school, and occupations of parents. 2 The Thurstone Temperament Schedule which has 140 items that are categorized into seven personality temperaments. 3 The General Aptitude Test Battery (GATB) Form B which identifies six areas of aptitude. Correlation coefficients were calculated between the final course grade and each of the three predictor measures stated above. The significant correlations between college GPA and high school GPA were 0.646 and 0.454, respectively, for the data collected from the fall 1975 term and the spring 1976 term (190). The significant correlations between high school rank were 0.514 and 0.353, respectively, for the data collected from the fall 1975 term and the spring 1976 term (190). The significant correlations between the GPA in high school mathematics and science were 0.568 and 0.435, respectively, for the data collected from the fall 1975 term and the spring 1976 term (190). They concluded that if a student has a high GPA in college, then that student will also do quite well in an introductory computer science course. If a student is still at a pre-college level, then the overall high school GPA and the high school mathematics and science GPA are good predictors of academic success in introductory computer science courses. Chapter 2/ 8 Fowler and Glorfeld (1981) created a classification model for classifying students as having a high or low aptitude for introductory computer courses. The measures used for collecting data were a questionnaire, the number of mathematics courses taken with a C grade or better, overall grade point average of all course work taken at A & M, final course mark, SAT Math grade, SAT verbal grade, and the 'Wolfe Programming Aptitude Test (WPAT) experimental form W (Wolfe, 1977)" (97) grade. Fowler and Glorfeld concluded that the most important variable in their model was GPA, followed by MATH, SATM, and AGE in decreasing order (among GPA, SATM, MATH, and AGE). They found that AGE has an opposite effect when compared to the other variables, showing that the lower the age, the greater chance of that student doing well in introductory computing. Stephens et al (1981) investigated the relationship between groups of students (grouped according to student characteristics) and their aptitude for an introductory computer science course. Data was collected by administering the "Computer Science Aptitude Pretest" (85) that contained a section on biographical information. They calculated a correlation coefficient of 0.47 (p < 0.001) between the computer science aptitude test and the final examination. Stephens et al (1981) concluded that student approximated high school achievement and student approximated current college achievement carried significant weight to performance on the computer science aptitude test (94). They concluded that on the ALG (Algorithmic Execution) section of the pretest, students with previous computer science experience scored significantly above students with no previous computer experience. Students 35 years old and older scored lower on the sections of "ALG (Algorithmic Execution) and TRANS (Alphanumeric Translation)" (86). A study done by Wileman et al (1982) examined the relationship between mathematical competencies and computer science achievement (measured by standardized tests) Chapter 2/ 9 rather than the ability to reason mathematically (which has been previously researched by Wileman et al (1981) and Howe (1979)). The following three tests were administered to all students: The Beckman-Beal Mathematical Competencies Test for Enlightened Citizens (Beckman-Beal test). The Konvalina-Stephens-Wileman Computer Science Aptitude Test (KSW), and a final course test. Pearson coefficient correlations were calculated between the KSW test and the 10 subsections of the Beckman-Beal test and the final exam and the 10 subsections of the Beckman-Beal test. The correlation between the KSW test and the Beckman-Beal test was 0.61 (p < 0.01). The correlation between the final examination and the Beckman-Beal test was 0.50 (p < 0.01). Wileman et al (1982) concluded that there is a strong relation between mathematical competencies and the probability of success in introductory computer science courses and that both tests (Beckman-Beal and KSW) can be used together for counseling students in senior secondary and college for computer science careers. Also, they contend that the level of mathematical competency of a student is a strong predictor of the level of computer science competency that a student may acquire. A study done by Hostetler (1983) investigated the predictability of cognitive skills, personality characteristics, and previous academic achievement level to achievement in an introductory computer science course. The 20 independent variables were two tests of Reasoning and Diagramming chosen from the Computer Programmer Aptitude Battery (CPAB) (1974), 16 personality traits from Form A of the Sixteen Personality Factor Questionnaire (16PF) (1979), post secondary grade point average (GPA), and prior mathematics background. The dependent variable was the final numerical grade achieved by the student at the end of the course. The three most significant predictors of computer science course success were the DIAGRAMMING test of the CPAB, the REASONING test of the CPAB, and the GPA having correlation coefficients of 0.480 (p < 0.01), 0.406 (p < 0.01), and 0.367 (p < 0.01) respectively. Chapter 2/ 10 Research done by Konvalina et al (1983) explored the relationship between eight factors (collected by a questionnaire) attached to the modified KSW (Konvalina, Stephens, and Wileman, 1981) Test and achievement of an introductory computer science course. They concluded the following: 1 For this specific sample population of students, 'The older the student, the higher the achievement score, except for the last age group, "35 or older." "(111). 2 A very good high level of high school performance predicts aptitude for computer science. 3 Prior non-trivial experience in computer science education has a significant relation to achievement in introductory computer science courses at the university or college level. This was found by a correlation coefficient of 0.18 (p < 0.01) between the final exam and PED (previous computer science education) and a Multiple R of0.53 (p< 0.110) between the KSW test and PED (111). 4 Avery good high level of high school mathematics performance predicts aptitude for computer science. To predict success in computer programming Nowaczyk (1983) administered a thirty minute pretest to collect data (background information, 18 statements from the Fennema and Sherman Mathematics Attitude Scale, and 9 statements from an Internal-External scale of personal locus of control (5-6)) and correlated it with the final course grades. He concluded that there was a significant correlation between the performance in the course and the following: (a) previous achievement in mathematics and English courses, (b) past computer experience, (c) expectation of grade, (d) and achievement on the given logic and mathematical word problems. Nowaczyk (1983) also concluded that the two strong major predictors of computer programming are previous academic performance and problem solving ability. Campbell and McCabe (1982, 1984) studied predictors of success of freshmen enrolled as computer science majors by reviewing their academic records. They concluded that students who persevered in computer science, engineering, or other sciences had significantly superior scores in SAT Math and SAT Verbal, had a higher ranking in Chapter 2/ 11 their high school graduation class, and had finished more semesters of high school science and mathematics with superior scores than students who transferred to to other divisions. Campbell and McCabe (1982, 1984) also concluded that gender was one of the most predictive variables (males had higher secondary SAT math grades and finished more semesters of high school science than females but had a lower ranking in their high school graduation class and had lower average scores in high school mathematics and English). A study designed by Sorge and Wark (1984) utilized historic data from the office of the registrar on computer science major students to identify and predict success or failure. The data that was analyzed included pre-college data and data collected at Purdue University. The data collected at Purdue University was an algebra-trigonometry exam that was used to place students in chemistry, mathematics, and computer science. They concluded that if a student was to have a good chance of making a passing grade or better in a computer science major at Purdue University, that student should have a minimum SAT mathematics score of 560, have a minimum SAT verbal score of 500, a minimum score of 5 on the algebra-trigonometry exam, rank in the top one-third of his/her high school class, and have a minimum of six terms of high school mathematics courses with an average of B- or better (44). Shoemaker (1986) carried out a research study at the University of California Irvine (UCI) to find predictor variables that would allow the admissions department to predict the performance of students in Engineering and Information and Computer Science (ICS). She concluded that high school GPA was the single best significant predictor for cumulative GPA of Information and Computer Science (ICS) students and for major GPA of ICS students, it was the second best predictor. She concluded that the College Board Mathematics Achievement Test (Level 1 or 2) was the single best significant Chapter 2/ 12 predictor for major GPA, and for cumulative GPA of Information and Computer Science (ICS) students it was the second best predictor. A study done by Greer (1986) examined the relationship between the achievement level in an introductory computer science course and the amount of structured computer science courses taken at high school. The following three instruments were administered to the students during the first week of classes: 1. The KSW Computer Science Aptitude Test designed by (Konvalina, Stephens, and Wileman, 1983). 2. The Raven's Advanced Progressive Matrices Test is from Buros (1972) that measures above normal intellectual ability. 3. A questionnaire designed by Greer (1986) that was used to collect biographical student data and a "Structured Programming Inventory" that collected information on how much structured programrning instruction the student had received in high school. Greer (1986) summarized that students with low ability who had experience in high school computer science courses had a greater chance of completing CMPT 110.6 with lower final grades but the low ability students that had no high school computer science experience had a greater chance of withdrawing from CMPT 110.6. In addition, he stated that high school computer science experience is not correlated with high achievement in university computer science courses (224). 2 College Level Hilleary (1966) conducted a study by administering a questionnaire to two classes of Business Data Processing students. Hilleary (1966) concluded that there was a greater percentage of males than females in these classes than the other classes and that the median age was 29. A study done by Jones (1979) determined the best combination of social, demographic, and academic variables to classify successful, unsuccessful, and dropout college students in introductory computer programming courses. A survey instrument that Chapter 2/ 13 focused on 18 chosen variables was adrninistered to all students. The final course grades and records were obtained from the college offices. Jones (1979) concluded that the most significant variable with the highest discriminating power was grade-point average (3). Griswold (1983) collected data from a class of education major students by using a 20 item questionnaire on computer awareness, a biographical survey (age, sex, and number of college level mathematics courses taken), a 10 question arithmetic basic skills quiz, and a locus of control instrument that had 29 items called "the Rotter I-E scale" (Rotter, 1966, 95). He concluded that the second best predictor was age which explained 6.6% of the variation (p < 0.005) (97-8) and the beta weight indicated that the older students tended to obtain higher scores for computer awareness. Gender was the third best predictor of computer awareness which explained 3.9% of the variance (p < 0.05) that reflected male students had greater computer awareness than female students. He concluded that the fourth best predictor of computer awareness was the number of mathematics courses taken that explained 4.1% of the variation (p < 0.05) (97-8). Ricardo (1983) studied the variables needed for an introductory PL/I programming course at the college level. The dependent variables were the score on the common final computer science examination and the final grade obtained. The data was collected by administering SAT (Scholastic Achievement Tests), reasoning tests, and a questionnaire (latter two measures were developed by Ricardo (1983)). After a statistical analysis, Ricardo (1983) concluded that verbal and mathematical aptitudes determined by the SAT (Scholastic Aptitude Test) verbal and mathematical grades significantly predicted success in the programming course using either the final score or the final grade. Chapter 2/ 14 Goodwin and Wilkes (1986) studied the correlation between computer science achievement and attitudes, some prior student factors, and previous computer experience. A questionnaire composed of items related to previous student characteristics and items related to computer attitudes and expectations was adrriinistered on the second class. The dependent variable was the final mark obtained in the course and the independent variables were the items on the questionnaire. Goodwin and Wilkes (1986) concluded the following: 1 A 0.25 increment in the previous knowledge of BASIC programming reflected a 0.25 increment in the final course mark and was the strongest predictor of course achievement (Beta Coefficient of 0.25). 2 The greatest predictor in the attitudes measure showed that the more the students expected difficulty in the course, the lower their final course mark; and if they expected very little difficulty, the higher their final course mark (Beta Coefficient of -0.15). 3 The Math SAT mark was the second strongest predictor of course achievement (Beta Coefficient of 0.18). 4 The larger the number of previous secondary Physics and Chemistry courses taken by a student, the lower the achievement level in this Pascal course (a Physics correlation coefficient = -0.22 and a Chemistry correlation coefficient = -0.17). A model for predicting success in introductory computer science students was developed by Oman (1986). All 38 students involved in the study completed the final examination and completed a survey requesting previous computer experience. Correlation coefficients calculated between the independent and dependent variables were 0.80 (p < 0.01) for MATHSAT, 0.65 (p < 0.01) for LANGS, 0.61 (p < 0.01) for VERBSAT, 0.60 (p < 0.01) for TIMESHARE, and 0.32 (p < 0.05) for MICROS. The stepwise multiple regression indicated that 82% of the variance in achievement for the two introductory computer science courses can be explained by the MATHSAT and previous computer experience. Oman (1986) developed a model to predict GRADEPTS by using the MATHSAT and the Chapter 2/ 15 other independent variables as "GRADEPTS = MATHSAT*.02 + YEARS*. 11 + VERBSAT*.22 + TTMESHARE*.6 + LANGS*.33 + MICROS*. 15" (232). GRADEPTS is the grade points, MATHSAT is the score on the mathematics Scholastic Aptitude Test (SAT), YEARS is the number of years after high school graduation, VERBSAT is the score on the verbal SAT, TIMESHARE is the number of computer time-sharing systems previously used, LANGS is the number of computer programming languages previously used, and MICROS is the number of microcomputers previously used (227-8). Further, he concludes that the above model can predict the GRADEPTS for a student within one grade for 70% of the pupils from student data collected in a 15 minute interview. Durndell et al (1987) examined previous experience with computers and gender with attitudes and interest in four different areas of study in a college. They collected the data by administering a questionnaire to first and final year students. They concluded that students in computer/electronic studies had more experience with computers than other students, males had more experience with computers than females, and males had more knowledge about computers than females. Cafolla (1988) investigated the correlation between cognitive development, mathematical reasoning, and verbal ability with the final course examination score. He administered the School and College Ability Test (SCAT II) to measure the verbal and mathematical levels and the Inventory of Piaget's Developmental Tasks (IPDT) to measure cognitive level. He concluded that cognitive development, mathematical reasoning, and verbal ability correlated significantly with computer programming ability. Furthermore, a regression analysis showed that a combination of cognitive development and verbal reasoning can be used to predict computer programming ability. Chapter 2/ 16 3 Secondary and College Level Alspaugh (1971) examined the relationship between grade levels and computer programming aptitude in FORTRAN programming at high school versus programming at college. There was no significant difference between both groups (high school and college) when scores from the Ohio Psychological Examination given by the Missouri Statewide Testing Program were compared. Both high school and college students "... were acirninistered the IBM Programmer Aptitude Test (McNamara & Hughes, 1969)" (45). The results of this aptitude test showed a significant difference between the college and high school students. To compensate for this difference, the same instructor doubled the instruction time for the high school students when teaching the introductory FORTRAN programming course. At the end of the course when both groups were administered the final examination, no significant difference was found between the groups. Alspaugh (1971) concluded that the programming aptitude score of a student increases with the level of the grade and that beginning courses in FORTRAN can be taught at grades 11-12 instead of grades 15-16 with similar achievement levels if the teaching time is increased (47). 4 Secondary Level Lockheed et al (1985) administered a Computer Literacy Test and a Computer Survey Questionnaire to students in grades nine to twelve. They found the following variables that were related to the gain in computer literacy: 1 gender (boys gained more than girls), 2 grade level (lower grade level students gained more than higher grade level students), 3 mathematics level (students taking higher level mathematics courses gained more than the others), 4 girls in grade nine and ten that had access to computers and previous experience with computers outside the school gained more than other students. Chapter 2/ 17 A study carried out by Szymczuk and Frerichs (1985) Investigated the predictive abilities of three standardized tests on achievement in an introductory high school computer science course. The Computer Programmer Aptitude Battery from the Science Research Associates Inc. and the Wolfe Programming Aptitude Test-Form W were administered on three consecutive class sessions starting on the second session. The grade nine Iowa Test of Educational Development was not administered but its results were collected since it is administered to all grade levels in the Fall. The Quantitative Thinking subtest of the Iowa Test of Educational Development was the best predictor of student achievement than any of the other subtests of the other standardized tests. The three subtests Quantitative Thinking, Reading, and Composite of the Iowa Test of Educational Development showed correlations of 0.77, 0.72, and 0.77 respectively. Szymczuk and Frerichs (1985) conclude that mathematics ability is very important for predicting success in computer science courses at high school (25). West et al (1985) analyzed the course taking patterns of high school students graduating in 1982. A national United States representative sample of 13 946 graduating seniors (1982) which were the sophomore class of 1980 was used for the data. They found that computer science credit (combination of computer science courses and business data processing courses) was earned by a minority of students (14% males and 11% females). The females were slightly underrepresented and the males were slightly overrepresented. They also concluded that computer science credit (combination of computer science courses and business data processing courses) was earned by students that had higher overall grade averages than other students. Computer science students were three times more likely to attend college or university (52% versus 32.2%) than other students. A one-year study done by Kurland et al (1986) attempted to establish transfer of learning between the types of cognition involved in computer programming with Chapter 2/ 18 mathematical reasoning skills. They also wanted to know the amount of programming knowledge retained by students and which predictors can be used to test for prograrnniing aptitude. The three kinds of pretests administered during the first month of classes were "... procedural reasoning, planning, and mathematics " (434). The posttests administered during the last month of classes were "... measures of procedural reasoning, decentering, planning, math ability, and algorithm design and comprehension" (437). Kurland et al (1986) calculated correlation coefficients between the pretest and the composite programming test for verbal procedural reasoning as 0.66 (p < 0.01) and for non-verbal procedural reasoning as 0.48 (p < 0.05). They also calculated correlation coefficients between the posttest and the composite programming test for verbal procedural reasoning as 0.65 (p < 0.01). Kurland et al (1986) calculated a correlation coefficient between the pretest and the composite programming test for mathematics as 0.77 (p < 0.01) and a correlation coefficient between the posttest and the composite programming test for mathematics as 0.77 (p < 0.01). Kurland et al (1986) concluded that there was a significant correlation between non-verbal and verbal procedural reasoning and computer programming scores. They also concluded that there was a significant correlation between mathematics and computer programming scores. Pommersheim and Bell (1986) and Pommersheim (1983) administered a questionnaire and seven cognitive profile tests to secondary students. They concluded that: 1 The male students had a higher achievement (p < 0.05) than the female students. 2 The male students had these advantages over the female students of already knowing a little BASIC before the course/knowing another programming language before the course, having extra information on BASIC other than the class, and having accessibility to computers outside of the classroom. 3 After the course was finished, only 10% of the female students continued to utilize and learn BASIC but 25% of the male students Chapter 2/ 19 further developed their skills. 4 After finishing BASIC, 25% of the male students enrolled in another programming course but only 10% of the female students enrolled in another programming course. 5 Students that had accessiblity to computers outside of the classroom, had higher computer programming course achievement. 6 Students that had extra information on the BASIC programming language other than the classroom, had higher computer programming course achievement. A survey study conducted by Schulz (1984) identified the desirable prerequisites of first year students enrolled in computer-related majors for 39 out of a possible 50 (78%) post-secondary institutions in Illinois. The data was collected by sending a questionnaire to each institution. Schulz (1984) found that the greatest prerequisite skill wanted by most was typing (29% great need for it and 67% stated it would be valuable to have). Schulz (1984) also concluded that a solid competency in communication skills and in mathematics is necessary to study computer science. Furthermore, after Schulz (1984) analyzed the data, he concluded that a knowledge of BASIC (9% need and 6 7 % valuable), FORTRAN (8% need and 64% valuable), and PASCAL (7% need and 6 3 % valuable) were the most desirable computer programming languages. The programming languages that needed to be taught the least at the secondary school level were PILOT (96%), MACHINE (89%), LOGO (87%), COBOL (84%), and ASSEMBLY (77%). The most wanted computer programming language prerequisites for introductory computer science courses at the college/university level were BASIC (30%), PASCAL (29%), and FORTRAN (29%). 5 Elementary and Secondary Level Webb (1985) did a study to examine individual versus group cognitive requirements for learning BASIC computer programming in a three hour workshop. Six pretests measuring reasoning, comprehension, inference, and field dependence/independence) Chapter 2/ 20 were administered. One posttest measuring knowledge of the BASIC programming language was administered. She calculated a correlation coefficient between age and the knowledge to interpret programs for groups as (0.46 (p < 0.01)). Webb concluded that in a group setting, age was a significant predictor of a minimum of one programming outcome. She calculated a correlation coefficient between verbal Inference and a knowledge of BASIC syntax for groups as 0.55 (p < 0.001) and for individuals as 0.59 (p < 0.001). She also calculated a correlation coefficient between verbal inference and the knowledge to generate a program for individuals as 0.51 (p < 0.001). Webb (1985) concluded that in a group setting and an Individual setting, verbal Inference was a significant predictor of a minimum of one programming outcome. She calculated a correlation coefficient between mathematics and a knowledge of BASIC syntax for groups as 0.53 (p < 0.001) and for Individuals as 0.67 (p < 0.001). She also calculated a correlation coefficient between mathematics and the knowledge to generate a program for Individuals as 0.60 (p < 0.001). Webb (1985) concluded that in a group setting and in an individual setting, mathematics was a significant predictor of a minimum of one programming outcome. Widmer and Parker (1985) used a survey Instrument to collect data of students competing in the annual International Computer Problem Solving Contest at the University of Wisconsin-Parkside in April 1984. They concluded the following characteristics of the students: (a) There was a large sex difference In the participants (248 males versus 28 females), (b) Boys that had programmed for more than two years made up 4 1 % of the total male participants but only 7% of the total girls had programmed as long and 9 3 % of the girls had just started to program 12 months before the contest, (c) Most students (77%) had taken a computer programming class at school, college, or at a computer outlet, (d) Almost one-third of the students (32%) had computer programming help available at home, (e) High contest achievement showed a Chapter 2/ 21 significant relation to students having taken a programming class, (f) The previous year's data (collected in 1983) showed a significant relation between high contest achievement and those students having computers at home, (g) Approximately two-thirds of all students were registered in mathematics classes and their reported grades were 66% got A's, 24% got B's, 8% got C s and 2% got D's. Becker and Sterling (1987) conducted a national survey entitled Second National Survey of Instructional Uses of School Computers by using questionnaires to collect data on the equity of computer use. They concluded that the usage of computers in elementary and secondary schools differs by the level of interest and ability of each student. More specifically, boys and students with higher achievement level and higher computer ability tended to dominate computer usage. 6 Elementary Level A three year longitudinal study done by Miura (1986) involving grades six, seven, and eight reflected the importance of gender in predicting interest in computer literacy. A questionnaire was used to collect the data. A regression analysis from all the data collected showed three things: 1 significant differences were found favoring males when predicting a high level of computer use and interest, 2 males tend to be more interactive with computers because of more available opportunities, more male role models to imitate, more encouragement verbally, and a smaller fear of the computers, and 3 males reflected a larger positive attitude in using computers to benefit our society needs than the females (3). b Summary The twenty-two pertinent predictor variables listed in Appendix J (excluding Variable # 19~Industrial Education achievement) were identified as being related to introductory computer science achievement and were extracted from the review of the literature. Chapter 2/ 22 (Note: Variable #19~Industrial Education Achievement is not indexed by any studies; the author chose to use this variable as an exploratory one.) The following table shows the predictor variables, the student level, and the number of studies: Chapter 2/ 23 TABLE I Summary bv Predictor Variables. Student Levels, and Number of  Studies Student Level Predictor Variables Univ Col Col+Sec SecSec+ElmElm 1 Gender I 3 4 2 2 Current Age 3 2 1 1 3 Year/Grade Level 1 1 4 No. of Yrs. After Secondary Graduation 1 5 No. of Math. Courses After Secondary 1 1 6 Secondary GPA 7 1 1 7 Current Col/Univ GPA 7 8 Prior Computer Science Achievement 4 9 Prior Computer Programming Experience 1 1 1 10 Current Help in Programming 2 1 11 Current Computer Access 3 1 12 Previous Computer Access 3 1 13 Expected Grade of Computer Course 1 1 14 Typing Achievement—Business Education 1 15 Secondary English Achievement 2 3 2 1 16 Secondary Languages Other Than English 1 17 Secondary Mathematics Achievement 9 4 4 2 18 Secondary Science Achievement 2 1 19 Secondary Industrial Education Achievem. 20 No. of Time-Sharing & Networking Systems 1 21 No. of Different Programming Languages 1 1 22 No. of Different Computers for Programmng 1 Totals: 39 20 1 23 11 1 Chapter 3/ 24 CHAPTER 3 METHOD a Pilot Study For the pilot study, the courses were chosen from the University of British Columbia 1987/88 Winter Session Calendar. CSED 217 (Section 002) and CSED 317 (Section 901) volunteer students were used which were registered in these courses from January-April 1988. A pilot study using CSED 217 (section 002) and CSED 317 (section 901) students was conducted prior to the main study to address the following factors: a Was the administration time of eight to ten minutes adequate for the questionnaire entitled Variables Predicting Achievement In Introductory Computer Science Courses (Appendix A)? b Was the administration time of 40 minutes adequate for the Computer Science Aptitude Examination (Appendix B)? c Were there any ambiguities in any of the questions on either instrument? d Were there any administration problems incurred in the administration of either measure? Both CSED 217 and CSED 317 classes were administered the questionnaire entitled Variables Predicting Achievement In Introductory Computer Science Courses (Appendix A) on March 22, 1988. There were four students in each class used for the pilot study. All students completed the questionnaire in seven to ten minutes. The CSED 217 class was also administered the Computer Science Aptitude Examination Chapter 3/ 25 (Appendix B) immediately after completing the first questionnaire. All students completed the examination in 40 minutes. No questions were raised by the students on the ambiguity of any question or the format on either of the two measures. The CSED 317 students pointed out that there was no question regarding the length of time since high school graduation and that Mathematics 12 was not listed. The following was implemented: (a.) Question #4 (number of years after secondary graduation) was added to the questionnaire (Appendix F). (b.) Mathematics 12 was added to the questionnaire in the instruction section as a similar course to Algebra 12. The questionnaire was administered first, and collected; then the examination (Appendix B) with the rough paper and answer sheet were handed out, administered and collected. The KSW Test was marked using the attached answer key (Appendix D). Approximately at the end of April, 1988, the final examination mark and the final course grade were obtained from the instructors for each course. b Description of students: Population and Sample The accessible population was all students registered in introductory computer science courses and in computing studies education courses at the University of British Columbia. The sample used for this study was students who volunteered to complete the questionnaire and were registered in introductory computer science courses at the University of British Columbia. All students met the minimum requirements for entrance to the University of British Columbia. There was a large Asian (Oriental) population which accounted for approximately one-third of the students who volunteered for the main study. Some of the Oriental students were directly from Asia and other students were later generation Canadians. Chapter 3/ 26 For the main study, the courses were similarly chosen from the University of British Columbia 1988/89 Winter Session Calendar. Most of the literature reviewed was based on introductory computer science courses at the university or college level, so this was the major criterion used for selecting these particular classes of students. All of the instructors involved in teaching the following courses were contacted by the researcher to obtain approval and cooperation in arranging a time and date for administering the survey instrument. Specifically, the courses selected for the main study with their brief descriptions from the University of British Columbia 1988/89 Calendar were CSED 420 section 901 (Computers for Instruction (with BASIC)), CSED 424 section 101 (Computers in the Secondary School (with PASCAL)), CPSC 101 section 101 (Introduction to Computer Programming), CPSC 114 section 101, 102, and section 901 (Principles of Computer Programming I), and CPSC 118 section 101 and 103 (Principles of Computer Programming) which were in session from September to December 1988. The cost of administering a questionnaire (Appendix A) that was not reusable was prohibitive for a sample size of approximately 600. An answer sheet was designed to be used with a reusable questionnaire booklet (Appendix F) to lower the administrative costs. The normal administration procedures for the KSW Test require that the students writing the examination cannot use calculators and must stop after 40 minutes. The instructors of CPSC 114 (sections 101 and 901) did not agree to let the researcher administer the KSW Test in class, but let the students take it home and restrict themselves to the 40 minutes with no use of calculators. The other six instructors did not agree to let the researcher actaiinister the KSW Test in class nor at home. CSED 420 and CSED 424 were administered the KSW Test in class but CPSC 114 (sections 101 and Chapter 3/ 27 901) completed the KSW Test as a take home examination and returned it later with an assignment for 10 bonus marks. The percentage of students volunteering for this study for each section are given below (All students that withdrew, that did not write the final examination, or that did not receive a final grade for the course were excluded from the total number of students in the sample.): 1 CSED 420 (section 901) 7 out of 7 (100%) 2 CSED 424 (section 901) 18 out of 18 (100%) 3 CPSC 101 (section 101) 85 out of 112 (75.9%) 4 CPSC 114 (section 101) 191 out of 198 (96.5%) 5 CPSC 114 (section 102) 103 out of 158 (65.2%) 6 CPSC 114 (section 901) 44 out of 44 (100%) 7 CPSC 118 (section 101) 112 out of 150 (74.7%) 8 CPSC 118 (section 103) 45 out of 55 (81.8%) The overall numbers and average of all sections of students that participated was 605 out of 742 (81.5%). c Research design and procedures The measure entitled Variables Predicting Achievement In Introductory Computer Science Courses (Appendix F) and the measure Computer Science Aptitude Examination (Appendix B) were administered during the first month of classes. Students completed the questionnaires at the end or beginning of their regularly scheduled classes. Instructions to all students were given orally and on an overhead transparency (Appendix E). The introductory section of the first instrument explained the rights and the consent of the student. Students choosing not to participate in the study, did not complete the questionnaire. If the questionnaire was completed, it was assumed that the student had given consent to participate in the study. Chapter 3/ 28 Approximately 10 minutes were given to complete the first instrument and no more than 40 minutes to complete the second instrument. When all of the students had completed the second instrument, the instructor and the researcher collected all of the completed and uncompleted questionnaires, the answer sheets, and the rough paper. The final examination score and the final course grade were obtained from the instructors for each of the eight classes used in the main study. d Data Analysis The answers from the questionnaire were grouped into 22 variables listed in Appendix J. Two further variables were measured: the total score on the questionnaire and the total score on the KSW Test. We had a total of 24 variables used in the statistical analyses. The two dependent variables were the course final examination score and the final course grade as determined by the instructors/professors. The 22 variables of the questionnaire were scored using the attached marking key (Appendix H). The basic premise for using the total score on the questionnaire as a predictor of computer science aptitude is related to the 18 selected background characteristics of the student. The background characteristics of the student are evaluated by the subscores of these 18 variables. These variables used for the subscores are related to achievement levels of specific secondary subject areas, grade point averages, access to computers, help available for programming, and expected grades. The higher the score of each of these 18 variables, the higher the total questionnaire score. Values given to each of these 18 variables are shown in Appendix H. Five routines were used to analyze the data using SPSSX (release 3.0) on the mainframe computer at the University of British Columbia as listed below: Chapter 3/ 29 1 Descriptive statistics The Frequencies program was run to show the mean, standard deviation, standard error, frequency, and the total valid percentage of students used in the study. Twenty-six variables were used for this program (22 variables from the questionnaire, total scores on the KSW Test and the questionnaire, the final course examination scores, and the final course grades). 2 Inferential statistics Pearson product-moment correlation coefficients were calculated between all the variables (excluding gender, current help in programming, current computer access, and previous computer access since they were nominal variables) and the two dependent variables (a 20 by 2 table). A 22 by 22 table of Pearson product-moment correlation coefficients among all the variables (excluding gender, current help in programming, current computer access, and previous computer access) is shown in Appendix I. Pearson product-moment correlation coefficients were calculated since most of the variables used interval or ratio scales and n (sample size of this study was 605) was larger than 50 (Hopkins and Glass, 1978, 120). A bivariate Multiple Regression was done to predict the final examination score and the final course grade from the total score on the questionnaire and from the total score on the KSW test. A multivariate Multiple Regression was done to predict the final examination score and the final course grade from the best combination and weightings of all the variables. The Multiple Regression equation results also showed the percentage of variance that contributed to predicting the final examination score and the final grade. Bivariate and multivariate stepwise multiple regression equations were used since n was substantially larger than 200 but n was not larger than 50 times the number of predictor variables (50 times 22 = 1100) (Hopkins and Glass, 1978, 170). A Fisher's Z-Chapter 3/ 30 transformation (Hopkins and Glass, 1978, 290) was done manually to calculate 9 5 % confidence intervals for the correlation coefficients calculated between the KSW Test score and the questionnaire score and the final examination score and the final grade. A Factor Analysis of all the variables was done to determine the most significant weightings of the variables that contributed to the overall score on the questionnaire. e Description of measures employed The KSW Test (from Konvalina et al, 1983, 379-81) was developed at the University of Nebraska at Omaha and is currently the latest and best test used for predicting computer science aptitude in introductory computer science courses at the college and university level. Permission to use the KSW Test for research purposes and a copy of the test were obtained from Dr. Larry Stephens. The original KSW examination was entitled Computer Science Placement Exam. For this study, the title was changed to Computer Science Aptitude Examination since this study was not directed at placing students but measuring their aptitude. The two diagrams on pages three and five of this examination were improved for clarity, professionalism, and size by using the graphics program "MACPAINT v. 1.5" on a Macintosh SE. Questions 34 and 35 were modified to use the metric system in (ounces were changed to grams and pounds were changed to kilograms) without changing the concepts of the problems and without changing the numeric values of the final answers. A separate answer sheet along with the examination booklet were supplied similar to the original examination package. All other administration procedures were kept identical to the original KSW Test (40 minute administration time, no calculators, and rough paper for hand calculations was supplied). The survey questionnaire (Appendix A) was developed by the author by combining all of the items that were thought to be important (from the studies done by previous Chapter 3/ 31 researchers) to establish predictor variables. Other items included on the survey questionnaire were cited in the review of the literature that showed significant correlations between the predictor variables and computer science achievement. This measure was designed to be a comprehensive composition of predictor items predicting computer science achievement as found through the review of the literature. The questionnaire was reviewed by Dr. Marvin Westrom and Dr. Stuart Dorm (both in the Division of Computing Studies Education, Faculty of Education, University of British Columbia) for editing, revisions, and a check for validity. According to Borg and Gall (1983), "Aptitude tests are aimed at predicting the student's later performance in a specific type of behaviour" (330). The two most commonly used aptitude tests they refer to are the General Aptitude Test Battery (GATB) and the Differential Aptitude Tests (DAT). When these two tests were examined closely, it was noticed that they take three hours or longer to administer and are more oriented to counselling in general rather than focusing in on the area of computer science achievement. The use of this questionnaire was considered more appropriate and relevant based on the time to administer it (10 to 12 minutes) and the maximum number of predictor variables utilized to gather the data. Some of the predictors are controversial since they have shown strong correlation with computer science achievement for some researchers but other researchers have found low or no correlation. This questionnaire was designed to investigate the relationship among the variables found and the strength of each correlation. There is always a large number of factors involved in the teaching and learning process. This means that there are factors affecting computer science achievement other than the predictor variables examined. These include teaching styles, student cognitive styles, course content, student social-economic backgrounds, student attitudes, etc. Chapter 3/ 32 (Nowaczyk, 1983). This may be an explanation why some researchers find their predictor variables strongly correlate with computer science achievement while other researchers find the same predictor variables have very low or no correlation. From this viewpoint, this questionnaire can be considered as an exploratory investigation of computer science predictors in the accessible population. A list of grade 12 and grade 11 courses that were offered in the 1987/88 school year was obtained from the Vancouver School Board Offices on December 29, 1987 to establish the number and types of secondary courses. The groups of subject areas and courses used in this questionnaire were selected from the Vancouver School Board List based on findings revealed in the review of the literature. British Columbia curriculum guides were consulted to establish the appropriate course content and the official Ministry of British Columbia subject names used by all schools in the province. The final compiled questionnaire is attached under Appendix F. The students answered directly on the answer sheet. The following table indexes all of the variables on the questionnaire with studies of significant predictors found in the previous research literature cited in Chapter 2 correlating computer science performance with predictor variables (variable #19— Industrial Education Achievement is not indexed by any studies since the author wanted to use this variable as an exploratory one): Chapter 3/ 33 TABLE H Questionnaire variables and indexed studies Variables Studies 1 Gender 2 Current Age 3 Year Level 4 Number of Years After Secondary Graduation 5 Number of Mathematics Courses After Grade 12 6 Secondary Grade Point Average 7 Current College/University Grade Point Average 8 Previous Computer Science Achievement 9 Previous Computer Programming Experience 10 Current Help in Programming 11 Current Computer Access 12 Previous Computer Access 13 Expected Grade of Current Computer Course Hilleary (1966), Griswold (1983), Pommersheim (1983), Campbell and McCabe (1982, 1984), Lockheed et al (1985), West et al (1985), Widmer and Parker (1985), Miura (1986), and Pommersheim and Bell (1986) Hilleary (1966), Fowler and Glorfeld (1981), Stephens et al (1981), Griswold (1983), Konvalina et al (1983), Lockheed et al (1985), Webb (1985) Alspaugh (1971) and Lockheed et al (1985) Oman (1986) and pilot study interviews with students (March 22, 1988) Fowler and Glorfeld (1981) and Griswold (1983) Petersen and Howe (1979), Stephens et al (1981), Konvalina et al (1983), Nowaczyk (1983), Campbell and McCabe (1982, 1984), Sorge and Wark (1984), West et al (1985), and Shoemaker (1986) Bauer et al (1968), Buff (1972), Jones (1979), Petersen and Howe (1979), Fowler and Glorfeld (1981), Stephens et al (1981), Hostetler (1983), and Nowaczyk (1983) Stephens et al (1981), Konvalina et al (1983), Nowaczyk (1983), and Greer (1986) Konvalina et al (1983), Widmer and Parker (1985), and Goodwin and Wilkes (1986) Pommersheim (1983), Widmer and Parker (1985), and Pommersheim and Bell (1986) Pommersheim (1983), Lockheed et al (1985), Widmer and Parker (1985), and Pommersheim and Bell (1986) Pommersheim (1983), Lockheed et al (1985), Widmer and Parker (1985), and Pommersheim and Bell (1986) Nowaczyk (1983) and Goodwin and Wilkes (1986) Chapter 3/ 34 TABLE II Continued Variables Studies 14 Typing Achievement 15 Secondary English Achievement Schulz (1984) Nowaczyk (1983), Ricardo (1983), Campbell and McCabe (1982, 1984), Schulz (1984), Webb (1985), Oman (1986), Kurland et al (1986) 16 Secondary Languages Other Than English 17 Secondary Mathematics Achievement Nowaczyk (1983) Alspaugh (1970, 1972), Schroeder (1978), Petersen and Howe (1979), Wileman et al (1982), Konvalina et al (1983) , Nowaczyk (1983), Ricardo (1983), Campbell and McCabe (1982, 1984), Schulz (1984), Sorge and Wark (1984) . Lockheed et al (1985), (1985) Widmer and Parker (1985), Szymczuk and Frerichs (1985), Kurland et al (1986), Oman (1986), Goodwin and Wilkes (1986), and Shoemaker (1986) 18 Secondary Science Achievement Petersen and Howe (1979), Campbell and McCabe (1982, 1984), and Goodwin and Wilkes (1986) 19 Industrial Education Achievement Author and thesis committee 20 Number of Time-Sharing and Networking Systems Previously Used For Programming 21 Number of Different Programming Languages Used For Programming 22 Number of Different Types of Computers Previously Used for Programming Oman (1986) Schulz (1984) and Oman (1986) Oman (1986) Chapter 4/ 35 CHAPTER 4 FINDINGS a Overview of data collection and analysis The data from the KSW Test and the questionnaire were entered onto Fortran Style coding forms. A total of 26 variables were used in the data collection (22 predictor variables are listed in Appendix J, the total score on the KSW Test, the total score on the questionnaire, the final course examination score, and the final course grade). Of these 26, the two dependent variables were the final examination score and the final course grade as determined by the instructors/professors. Variables 1, 10, 11, and 12 (Gender, Current Help in Programming, Current Computer Access, and Previous Computer Access) were nominal variables and were not used for calculating Pearson product-moment correlation coefficients, multiple regression equations, and factor analysis. b Description of findings pertinent to each hypothesis The four research questions were: 1 Which secondary subject area(s) final course grades correlate significantly with the final course examination scores and/or the final course grades of introductory computer science courses? 2 Which other variables or characteristics (such as number of mathematics courses and expected grade), correlate significantly with the final course examination scores and/or the final course grades in introductory computer science courses? 3. Will the total scores from the newly developed questionnaire correlate higher than the KSW Test scores with the final course examination scores and/or the final course grades in introductory computer science courses? Chapter 4/ 36 4 Is it possible to develop effective equations to predict the final course examination scores and the final course grades for introductory computer science courses based on the total scores from the questionnaire and/or the total scores from the KSW Test? Table III and Table IV show calculations for the descriptive and inferential statistics. The descriptive statistics calculations done to show the means, standard deviations, standard errors, frequencies, and the percentage of valid students are given below: Chapter 4/ 37 1 Descriptive Statistics TABLE HI Descriptive Statistics of All 26 Variables Variables Mean Std. Dev. Std. Err. Freq. Valid 1 1 Gender: a) Females 174 25.9 b) Males 498 74.0 Total 672 99.9 2 Current Age 20.3 4.12 0.160 661 98.2 3 Year/Grade Level 2.04 1.22 0.047 662 98.4 4 No. of Years After Secondary Grad. 2.58 3.80 0.149 654 97.2 5 No. of Math. Courses After Secondary 2.10 2.34 0.091 662 98.4 6 Secondary Grade Point Average 4.02 0.839 0.033 662 98.4 7 Current College/Univ. GPA 1.94 1.48 0.058 662 98.4 8 Prior Computer Science Achievement 4.59 5.20 0.202 662 98.4 9 Prior Computer Programming Exp. 4.29 6.20 0.241 662 98.4 10 Cr. Help in Prog.: a) Yes 341 51.5 b) No 321 48.5 Total 662 98.4 11 Current Computer Access: a) Yes 356 52.9 b) No 306 45.5 Total 662 98.4 12 Previous Computer Access: a) Yes (K-7 or 8-12) 263 39.1 b) Yes (K-7 & 8-12) 36 5.3 c) No 363 53.9 Total 662 98.4 Chapter 4/ 38 TABLE m Continued Variables Mean Std. Dev. Std. Err. Freq. Valid % 13 Expected Grade of Current Course 3.42 0.617 0.024 660 98.1 14 Secondary Typing Achievement 1.09 2.25 0.088 662 98.4 15 Secondary English Achievement 7.18 2.29 0.089 662 98.4 16 Sec. Languages Other Than English 4.97 3.29 0.128 662 98.4 17 Secondary Mathematics Achievement 9.94 4.10 0.159 662 98.4 18 Secondary Science Achievement 17.1 7.29 0.283 662 98.4 19 Industrial Education Achievement 3.11 5.56 0.216 662 98.4 20 No. of Tlme-Sharing+Networklng Sys. 0.577 1.03 0.040 662 98.4 21 No. of Diff. Programming Languages 1.73 1.52 0.059 669 99.4 22 No. of Diff. Types of Computers Used 1.66 1.52 0.059 669 99.4 23 Score on KSW Test 17.9 3.56 0.219 265 39.4 24 Score on Questionnaire 69.5 20.9 0.817 660 98.1 25 Final Course Examination Score 58.9 20.6 0.831 616 91.5 26 Final Course Grade 48.9 12.7 0.506 626 93.0 There were more males than females who participated in the study (498 versus 174). This was due to the fact that more males than females were registered in the introductory computer science courses. Approximately the same number of students had current help in programming at home while taking the computer science course versus the number that did not (341 versus 321). Approximately an equal number of students had access to a computer at home versus the number that did not (356 versus 306). About one-half (363 or 53.9%) of the students did not have previous access to a computer at home while they attended school (kindergarten to grade 12); just over a third (263 or 39.1%) of the students had access to a computer at home while they attended elementary (K-grade 7) or secondary school (grade 8-grade 12); and only a small number (36 or 5.3%) had access to a computer at home while they attended Chapter 4/ 39 elementary and secondary school. The graph below shows the distribution of variable 4 (No. Yrs. Aft Sec): Figure 1 Distribution Curve For Number of Years After Secondary Graduation Distribution of Years After Sec. Grad. 220 0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 6 1 7 1 8 1 9 2 1 22 24 26 27 Number of Years In Table III, variables 4, 8, 9, 14, and 19 (No. Yrs. Aft. Sec, Prv. Cmp. Sc. Ach., Prv. Cmp. Prg. Exp., Typing Ach., and Industrial Ed. Ach.) had larger standard deviations than their respective means. This showed that these variables had highly skewed distributions. Under a normal distribution curve, plus or minus three standard deviations around the mean is usually standard for this size of n. To show evidence that the sample used in this study was representative of the total accessible population, means of the final course examinations and the final course grades were calculated for both groups. From Table III the mean of the final examination was 58.9 and the mean of the final grade was 48.9 for the sample. The Chapter 4/ 40 group of 137 students that did not volunteer for the study had a mean of 56.8 for the final examination and a mean of 47.3 for the final grade. Note that these means from the above table show frequencies of 616 and 626 but only 605 students who volunteered for the study wrote the final examination. This means that some students were given a final grade although they did not write the final examination. 2 Inferential Statistics The Pearson product-moment correlation coefficients that were calculated are shown in Table IV below and Table XI in Appendix I: Chapter 4 /41 TABLE IV Pearson Product-Moment Correlation Coefficients For All Eight  Sections Dependent Variables Independent Variables Final Examination Final Grade 2 Current Age -0.0432 0.1119* (N = 605) (N = 615) P = 0.144 P = 0.003 3 Year Level 0.0087 0.1890* (N = 606) (N = 616) P = 0.415 P< 0.001 4 Number of Years After 0.0145 0.1142* Secondary Graduation (N = 598) (N = 608) P = 0.362 P = 0.002 5 Number of Mathematics 0.1522* 0.1858* Courses Completed After (N = 606) (N = 616) Secondary Graduation P< 0.001 P < 0.001 6 Secondary Grade Point 0.2796* 0.2559* Average (N = 606) (N = 616) P< 0.001 P< 0.001 7 Current College/University 0.1742* 0.2114* Grade Point Average (N = 606). (N = 616) P< 0.001 P< 0.001 8 Prior Computer Science 0.1775* 0.1284* Achievement (N = 606) (N = 616) P < 0.001 P< 0.001 9 Prior Computer 0.2040* 0.1958* Programming Experience (N = 606) (N = 616) P< 0.001 P< 0.001 13 Expected Grade of Current 0.2979* 0.2989* Computer Course (N = 606) (N = 616) P< 0.001 P< 0.001 Chapter 4/ 42 TABLE IV Continued Independent Variables Final Examination Final Grade 14 Typing Achievement -0.0541 -0.0630 (N = 606) (N = 616) P = 0.092 P = 0.059 15 Secondary English 0.1344* 0.1319* Achievement (N = 606) (N = 616) P< 0.001 P = 0.001 16 Secondary Languages 0.0920* 0.0689* Other Than English (N = 606) (N = 616) Achievement P = 0.012 P = 0.044 17 Secondary Mathematics 0.1735* 0.1589* Achievement (N = 606) (N = 616) P< 0.001 P< 0.001 18 Secondary Science 0.2146* 0.1812* Achievement (N = 606) (N = 616) P < 0.001 P < 0.001 19 Industrial Education -0.0275 -0.0320 Achievement (N = 606) (N = 616) P = 0.250 P = 0.214 20 Number of Time-Sharing 0.1218* 0.1702* and Networking Systems (N = 606) (N = 616) Previously Used P = 0.001 P< 0.001 21 Number of Different 0.2681* 0.2872* Programming Languages (N = 613) (N = 623) Used for Programming P < 0.001 P< 0.001 22 Number of Different T y p e s 0.1611* 0.1819* of Computers Previously (N = 613) (N = 623) Used for Programming P < 0.001 P < 0.001 23 Total Score on KSW Test 0.2644* 0.2638* (N = 246) (N = 252) P < 0.001 P < 0.001 24 Total Score on 0.3267* 0.3004* Questionnaire (N = 605) (N = 615) P< 0.001 P < 0.001 *p < 0.05 or *p < 0.003 (Using the Bonferroni correction: 0.05 + 20 « 0.003) Chapter 4/ 43 3 Discussion of Research Questions From Table IV, the secondary subject areas that correlated significantly with final examination score and final course grade were prior computer science achievement, secondary English achievement, secondary languages other than English, secondary mathematics achievement, and secondary science achievement. Furthermore, from the above table, other student characteristics that correlated significantly with the final examination scores were number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. Other characteristics that correlated significantly with the final course grades were current age, year/grade level number of years after secondary graduation, number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college /university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. The KSW Test was not administered in class but was completed at home and returned for CPSC 114 (sections 101 and 901) where the students monitored their Chapter 4/ 44 own time limit of 40 minutes and were not supposed to use calculators. The KSW Test was administered in class for CSED 420 (section 901), CSED 424 (section 901), and CSED 217 (section 002). The KSW Test scores and the questionnaire scores correlated significantly with the final course examination scores and the final course grades. Four Fisher's Z-transformations were done to calculate the 9 5 % confidence intervals for the correlation coefficients calculated between the KSW Test score and the Questionnaire score and the final examination score and the final grade. These calculations were done to determine If the Pearson r's calculated in Table IV using the KSW Test and the Questionnaire were significantly different from one another in the target population. The four values are given in the table below: TABLE V Fisher's Z-transformations Population Variables Pearson r Lowest Value Highest Value KSW Test & final course grade 0.264 0.143 0.377 KSW Test & final course examination 0.264 0.143 0.377 questionnaire & final course grade 0.300 0.228 0.370 questionnaire & final course examination 0.327 0.253 0.396 These four calculations show that there was considerable overlap among the four Intervals surrounding the four r's. This means that there was no Chapter 4/ 45 significant difference among the r's (in Table IV) for the total KSW Test score and the total Questionnaire score in the accessible population and that one predictor measure was not better than the other. 4 Equations using the bivariate multiple regression were developed to predict the final course examination score and the final course grade when given the questionnaire score and the KSW Test score. The four prediction equations were: 4 Bivariate multiple regression equations 1 Final Grade = (0.937)(KSW Test) + 32.1 [R Square = 0.069, Standard Error = 12.2, F= 18.7, Signif. P< 0.001) 2 Final Exam = (1.53)(KSWTest) + 31.5 (R Square = 0.069, Standard Error = 19.9, F= 18.3, Signif. P< 0.001) 3 Final Grade = (0.181)(Questionnaire) + 36.3 [R Square = 0.090, Standard Error = 12.1, F= 60.8, Signif. P < 0.001) 4 Final Exam = (0.321)(Questionnaire) + 36.6 (R Square = 0.107, Standard Error = 19.5, F= 72.1, Signif. P < 0.001) Chapter 4/ 46 The final course grade ±12.2 will be accurate 68% of the time when the KSW Test score is given. The standard error of 12.2 used for predicting the final course grade was calculated in the derivation of the bivariate multiple regression equation. The final course examination score ± 19.9 will be accurate 68% of the time when given the KSW Test score. The standard error of 19.9 used for predicting the final course examination score was calculated in the derivation of the bivariate multiple regression equation. The final course grade ±12.1 will be accurate 68% of the time when the questionnaire score is given. The standard error of 12.1 used for predicting the final course grade was calculated in the derivation of the bivariate multiple regression equation. The final examination score ±19.5 will be accurate 68% of the time when the questionnaire score is given. The standard error of 19.5 used for predicting the final course examination score was calculated in the derivation of the bivariate multiple regression equation. An increase of 1 in the KSW Test will increase the final grade by 0.937 (from B = 0.937) and increase the final examination score by 1.53 (from B = 1.53). An increase of 1 in the Questionnaire will increase the final grade by 0.181 (from B -0.181) and increase the final examination score by 0.321 (from B = 0.321). The final course examination score and the final course grade had approximately 7% of the variance explained by the relationship between each of them and the KSW Test score (from R square = 0.069). The final course examination score had approximately 11% of the variance explained by the total questionnaire score (from R square = 0.107). The final course grade had approximately 9% of the variance explained by the total questionnaire score (from R square = 0.090). Equations using the stepwise multivariate multiple regression were developed to predict the final course examination score and the final course grade when given Chapter 4/ 47 all 22 variables (excluding gender, current help in programming, current computer access, and previous computer access). The two prediction equations were: 5 Multivariate Multiple Regression Equations 1 Final Grade = (4.68)[expected grade) + [2A3)[year/grade level) + (1.97)(no. of different programming languages) + (1.11) [English achievement) + (0.533) (KSW Test) + (0.121) (Questionnaire) -1.27 [R Square = 0.297, Standard Error = 11.9, F= 16.2, Signif. P < 0.001; B and Standard Error of B values for the six variables are given in the table below:) TABLE VI Parameters of the Final Grade Equation Variables B S E B 1 Expected grade of current computer course 4.68 1.45 2 Year/grade level 2.43 0.562 3 No. of diff. programming languages used for programming 1.97 0.744 4 Secondary English achievement 1.11 0.403 5 Score on KSW Test 0.533 0.234 6 Score on Questionnaire 0.121 0.064 The final grade ± 11.9 will be accurate 68% of the time when the expected grade, year level, number cf different programming languages, secondary English Chapter 4/ 48 achievement, the KSW Test score, and the Questionnaire score are given. The standard error of 11.2 used for predicting the final course grade was calculated in the derivation of the multivariate multiple regression equation. The final course grade score had approximately 3 0 % of the variance that was predictable from the six variables listed above. 2 Final Exam = (6.77){expectedgrade) + (1.20)(KSWTest) + (0.378)(Questionnaire) - 13.1 [R Square = 0.182, Standard Error = 20.9, F= 16.8, Signif. P < 0.001; B and Standard Error of B values for the three variables are given in the table below:) TABLE VII Parameters of the Final Examination Equation Variables B SE B 1 Expected grade of current computer course 6.77 2.56 2 Score on KSW Test 1.20 0.412 3 Score on Questionnaire 0.378 0.101 The final course examination score ± 20.9 will be accurate 68% of the time when given the expected grade, the KSW Test score, and the Questionnaire score are given. The standard error of 20.9 used for predicting the final course examination score was calculated in the derivation of the multivariate multiple regression equation. The final course examination score had approximately Chapter 4/ 49 18% of the proportion of variance that was predictable from the three variables listed above. c Other findings A Factor Analysis of all the variables was calculated to determine the most significant weightings or the most significant variables that contributed to the overall score on the questionnaire. From the 22 variables used in the questionnaire, only 15 variables qualified to be used in the factor analysis (first four variables were not used to calculate the total questionnaire score and three variables were nominal that had just values of 0, 1 or 2). From these 15 variables, five factors were formulated where each factor was composed of a linear composition of one or more variable. The available results that were calculated are given below under the appropriate headings: TABLE VIII Percentage of Variance of the Factors Factor Number Eigenvalue Percent of Variance Cum. Percent 1 3.21 21.4 21.4 2 2.05 13.6 35.0 3 1.73 11.5 46.5 4 1.21 8.00 54.6 5 1.06 7.10 61.7 Chapter 4/ 50 TABLE IX Weightings of Each Variable on Each Factor Factor Numbers Variables 1 2 3 4 5 5 No. of Math. Courses After Secondary Grad. 0.220 -0.445 0.708* 0.085 0.050 6 Secondary Grade Point Average 0.499* 0.567* 0.193 -0.055 -0.063 7 Current College/University GPA 0.037 -0.316 0.720* 0.316 -0.002 8 Prior Computer Science Achievement 0.587* 0.071 -0.432 0.137 0.131 9 Prior Computer Programming Experience 0.660* -0.187 -0.314 -0.013 0.322 13 Expected Gradeof Current Computer Crse. 0.547* 0.010 0.019 -0.251 -0.154 14 Secondary Typing Achievement -0.031 0.049 0.115 0.433 0.527* 15 Secondary English Achievement 0.241 0.644* 0.034 0.339 0.070 16 Sec. Languages Other Than English 0.140 0.540* 0.025 0.499* 0.064 19 Secondary Industrial Education Achv. 0.193 -0.284 -0.012 -0.280 0.699* 20 No. of Time-Sharing+Networking Systems 0.508* -0.331 0.090 0.307 -0.209 21 No. of Diff. Programming Languages Used 0.769* -0.359 -0.120 0.071 -0.145 22 No. of Diff. Types of Computers Used 0.708* -0.223 -0.178 0.111 -0.222 *weighting > 0.5 Each of the five factors was composed of one or more variable used in the questionnaire. The five factors were a linear composition of some of the selected 15 Chapter 4/ 51 variables used in the questionnaire (VPAIICSC) where a weighting of 0.5 or higher was used for the selection. By examining the composition of each factor based on the variables, the following new names were given to the five factors: Factor 1—Computer Literacy Factor 1—Scholastic Achievement and Language Learning Aptitude Factor 3—Mathematical Reasoning and Scholastic Achievement Factor 4—Language Learning Aptitude Factor 5--Ftnger Dexterity These five factors below are five new independent variables: FACTOR 1 = (0.587) Computer science achievement + (0.660)Programming experience + (0.547)Expected grade + (0.508) Networking experience + (0.769) Different computer programming languages+ (0.708)Different computers FACTOR 2 (0.567)Secondary GPA + {0.644)English achievement + (0.540)Languages other than English FACTOR 3 (0.708)JVo. of mathematics courses after secondary graduation + (0.720) Current college/university GPA FACTOR 4 (0.499)Languages other than English FACTOR 5 (0.527)Typing achievement + {0.699)Industrial Education achievement Chapter 4/ 52 To show that the 20 predictor variables were independent and had low correlation among each other, the following bar graph was plotted using data from Appendix I: Figure 2 Frequencies of Significant Pearson Correlation Coefficients Frequencies of Significant Pearson r's F r e q u e n c i e s 40 36 32 28 24 20 16 12. 8 4 0 1 1 1 I 1 I I f 1 T71 T 1 -0.9 -0,7 -0.5 -0.3 -0.1 0 0.10.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Pearson r's Chapter 5/ 53 CHAPTER 5 SUMMARY AND DISCUSSION a Summary of research problem, method, and findings 1 Research Problem Instructors have no way of predicting their students' achievement. This is one problem in the teaching and learning of computer science. Introductory computer science courses usually do not have any prerequisites. One way of solving this problem was to develop an instrument that would correlate significantly with the final course examination score and the final course grade. This instrument was developed in this study. Based on the results of this instrument, a different course or direction may be suggested for some students whereas other students would be encouraged to remain in the course (Petersen and Howe, 1979, 184). A questionnaire was created by using 22 predictor variables identified in the review of the literature. Each variable was correlated with the final course examination score and the final course grade to establish statistically significant predictors. 2 Method 3 Findings 1 The five secondary subject areas that correlated significantly with the final examination scores and the final course grades were: 1 Prior computer science achievement 2 Secondary English achievement 3 Secondary languages other than English Chapter 5 / 5 4 4 Secondary mathematics achievement 5 Secondary science achievement Eight other variables that correlated significantly with the final examination scores were: 1 Number of mathematics courses completed after grade 12 2 Overall grade 12 percentage/grade point average 3 Current college /university overall percentage/grade point average 4 Prior computer programming experience 5 Expected grade of current computer course 6 Number of timesharing and networking systems previously used 7 Number of different programming languages used for programming 8 Number of different types of computers previously used for programming Eleven other variables that correlated significantly with the final course grades were: Current age 2 Year/grade level 3 Number of years after secondary graduation 4 Number of mathematics courses completed after grade 12 5 Overall grade 12 percentage/grade point average 6 Current college/university overall percentage/grade point average 7 Prior computer programming experience 8 Expected grade of current computer course 9 Number of timesharing and networking systems previously used Chapter 5/ 55 10 Number of different programming languages used for programming 11 Number of different types of computers previously used for programming 3 The KSW Test scores and the questionnaire scores correlated significantly with the final course examination scores and the final course grades. 4 Equations using the bivariate multiple regression were developed to predict the final course examination score and the final course grade when given the questionnaire score or the KSW Test score. Equations using the stepwise multivariate multiple regression were developed to predict the final course examination score and the final course grade when given all 20 variables (excluding gender, current help in programming, current computer access, and previous computer access). 4 Other Findings A Factor Analysis of all the variables was calculated to determine the most significant weightings or the most significant variables that contributed to the overall score on the questionnaire. By examining the composition of each factor based on the variables, the following new names were given to the five factors: Factor 1—Computer Literacy Factor 2--Intelligence and Language Learning Aptitude Factor 3~Mathematical Reasoning and Intelligence Factor 4—Language Learning Aptitude Factor 5—Finger Dexterity Chapter 5/ 56 b Interpretations of the Findings 1 Secondary Subject Areas The five secondary subject areas that correlated significantly with the final course examination scores and the final course grades were prior computer science achievement secondary English achievement secondary languages other than English, secondary mathematics achievement, and secondary science achievement. They are listed below for interpretation: 1 The significant correlation of prior computer science achievement is consistent with the findings of Stephens et al (1981), Konvalina et al (1983), Nowaczyk (1983), and Greer (1986). This correlation seems to be obvious since one would expect students to have high achievement in introductory computer science courses if they had high achievement in secondary computer science courses. 2 The significant correlation of secondary English achievement is similar to the findings of Nowaczyk (1983), Ricardo (1983), Campbell and McCabe (1982, 1984), Schulz (1984), Webb (1985), Oman (1986), Kurland et al (1986), and Cafolla (1988). This finding is somewhat interesting since English is not usually grouped with mathematics and computer science; although a strong verbal or communication skill is required to fully comprehend programming problems and to create proper documentation to thoroughly explain the solutions. 3 The significant correlation of secondary languages other than English is not supported by any of the researchers. This finding is somewhat novel but if one compares the learning of the English language and the learning of another language other than English (foreign language) with the learning of computer languages, there would appear to be some similarities. Chapter 5/ 57 4 The significant correlation of secondary mathematics achievement is reinforced by Alspaugh (1970, 1972), Schroeder (1978), Petersen and Howe (1979), Wileman et al (1982), Konvalina et al (1983), Nowaczyk (1983), Ricardo (1983), Campbell and McCabe (1982, 1984), Schulz (1984), Sorge and Wark (1984), Lockheed et al (1985), Webb (1985) Widmer and Parker (1985), Szymczuk and Frerichs (1985), Kurland et al (1986), Oman (1986), Goodwin and Wilkes (1986), Shoemaker (1986), and Cafolla (1988). This finding is not new since all of the above researchers have concluded the same and the type of logic involved in learning mathematics is parallel to the type of logic needed to learn computer programming. 5 The significant correlation of secondary science achievement is upheld by Petersen and Howe (1979) and Campbell and McCabe (1982, 1984). This finding is in line with what one would expect since mathematics is an underlying component of most secondary science curriculums and mathematics is one of the factors that correlates significantly. 2 Characteristics Other Than Secondary Subject Areas The eight other variables that correlated significantly with the final examination scores were number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. They are listed below with the interpretations: Chapter 5/ 58 1 The significant correlation of number of mathematics courses completed after grade 12 is supported by Fowler and Glorfeld (1981) and Griswold (1983). This finding is not surprising since it is also related to mathematics achievement. 2 The significant correlation of overall grade 12 percentage/grade point average is also concluded by Petersen and Howe (1979), Stephens et al (1981), Konvalina et al (1983), Nowaczyk (1983), Campbell and McCabe (1982, 1984), Sorge and Wark (1984), West et al (1985), and Shoemaker (1986). This shows that overall achievement in secondary school reflects good work habits, aptitude, and proper attitudes towards learning which are necessary in any post secondary field of study including computer science. 3 The significant correlation of current college/university overall percentage/grade point average is upheld by Bauer et al (1968), Buff (1972), Jones (1979), Petersen and Howe (1979), Fowler and Glorfeld (1981), Stephens et al (1981), Hosteller (1983), and Nowaczyk (1983). This shows that overall intelligence in college/university reflects work habits and attitudes towards learning which are appropriate to post secondary education including computer science. 4 The significant correlation of prior computer programming experience is also confirmed by Konvalina et al (1983), Widmer and Parker (1985), and Goodwin and Wilkes (1986). This finding seems quite obvious and logical since most introductory computer science courses have a major component of programming in the content of the course. 5 The significant correlation of expected grade of current computer course is upheld by Nowaczyk (1983) and Goodwin and Wilkes (1986). This finding is interesting since it is in the area of self efficacy or self confidence, people that expect to do well actually do achieve higher and people that expect not to do well achieve lower. Chapter 5/ 59 6 The significant correlation of number of timesharing and networking systems previously used is also confirmed by Oman (1986). This finding is not surprising since it is related to previous computer programming experience and overall general computer literacy or knowledge of computer which would reduce computer phobia. 7 The significant correlation of number of different programming languages used for programming is similar to the findings of Schulz (1984) and Oman (1986). This finding is not surprising since it is a logical presumption and it is related to prior computer programming experience. 8 The significant correlation of number of different types of computers previously used for programming is similar to the conclusions of Oman (1986). This finding is related to prior computer programming experience, previous computer access, and the number of different programming languages used for programming. All of these variables are logical assumptions one would make to predict computer science achievement. The 11 other characteristics that correlated significantly with the final course grades were current age, year/grade level number of years after graduation, number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage /grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. 1 The significant correlation of age was also uncovered by Hilleary (1966), Fowler and Glorfeld (1981), Stephens et al (1981), Griswold (1983), Konvalina et al (1983), Lockheed et al (1985), and Webb (1985). This indicates that older/mature students tend Chapter 5/ 60 to be more conscientious and have developed better studying and learning habits than younger students. 2 The significant correlation of year/grade level was also detected by Alspaugh (1971) and Lockheed et al (1985). Presumably, the aptitude for computer science increases with maturity and maturity has an effect on the areas of cognitive development needed for learning computer science. 3 The significant correlation of number of years after secondary graduation was also supported by Oman (1986). This finding indicated that more mature or older students, on average, seem to be more dedicated to their studies in computer science than younger students. 4 The significant correlation of number of mathematics courses completed after grade 12 is supported by Fowler and Glorfeld (1981) and Griswold (1983). This finding is not surprising since it is also related to mathematics and mathematical reasoning which are parallel to the types of reasoning required in computer science. 5 The significant correlation of overall grade 12 percentage/grade point average is also concluded by Petersen and Howe (1979), Stephens et al (1981), Konvalina et al (1983), Nowaczyk (1983), Campbell and McCabe (1982, 1984), Sorge and Wark (1984), West et al (1985), and Shoemaker (1986). This shows that overall intelligence in secondary school reflects good work habits and proper attitudes towards learning which are necessary in any post secondary field of study Including computer science. 6 The significant correlation of current college/university overall percentage/grade point average is upheld by Bauer et al (1968), Buff (1972), Jones (1979), Petersen and Howe (1979), Fowler and Glorfeld (1981), Stephens et al (1981), Hostetler (1983), and Nowaczyk (1983). This shows that overall achievement in secondary school Chapter 5/61 reflects good work habits and proper attitudes towards learning which are necessary in any post secondary field of study including computer science. 7 The significant correlation of prior computer programming experience is also confirmed by Konvalina et al (1983), Widmer and Parker (1985), and Goodwin and Wilkes (1986). This finding seems quite obvious and logical since most introductory computer science courses have a major component of programming in the content of the course. 8 The significant correlation of expected grade of current computer course is upheld by Nowaczyk (1983) and Goodwin and Wilkes (1986). This finding is interesting since it is in the area of self efficacy or self confidence, people that expect to do well actually do achieve higher and people that expect not to do well achieve lower. 9 The significant correlation of number of time-sharing and networking systems previously used is also confirmed by Oman (1986). This finding is not surprising since it is related to previous computer programming experience and overall general computer literacy or knowledge of computer which would reduce computer phobia. 10 The significant correlation of number of different programming languages used for programming is similar to the findings of Schulz (1984) and Oman (1986). This finding is not surprising since it is a logical premise one would make; relating prior computer programming experience with the final course grade. 11 The significant correlation of number of different types of computers previously used for programming is similar to the conclusions of Oman (1986). This finding is related to prior computer programming experience, previous computer access, and the number of different programming languages used for programming. All of these Chapter 5/ 62 variables are logical assumptions one would make to predict computer science achievement. 3 Discussion of the Descriptive Statistics There were more males than females registered in the accessible population. This meant there would be more males than females who took part in the study (498 versus 174). This finding is upheld by Hilleary (1966), West el al (1985), and Widmer and Parker (1985) who stated that more males than females tended to register in computer science courses. Approximately the same number of students had current help in programming at home while taking the computer science course versus the number that did not (341 versus 321). Pommersheim (1983), Widmer and Parker (1985), and Pommersheim and Bell (1986), had concluded that students who had programming help other than the classroom had higher computer programming course achievement. Approximately an equal number of students had access to a computer at home versus the number that did not (356 versus 306). About one-half (363 or 53.9%) of the students did not have previous access to a computer at home while they attended school (kindergarten to grade 12); just over a third (263 or 39.1%) of the students had access to a computer at home while they attended elementary (K-grade 7) or secondary school (grade 8-grade 12); and only a small number (36 or 5.3%) had access to a computer at home while they attended elementary and secondary school. Pommersheim (1983), Widmer and Parker (1985), Lockheed et al (1985), and Pommersheim and Bell (1986), concluded that students who had access to computers at home had higher computer programming achievement and gained more computer literacy. Chapter 5/ 63 4 Predictability of Questionnaire Scores Versus KSW Test Scores Including the pilot study and the main study, 10 sections of students were tested. The questionnaire developed by the researcher was administered to all 10 sections during class time (completion time varied from 7 to 13 minutes). The KSW Test was administered to three sections during class which were monitored to the 40 minute time limit and the examinees were not allowed to use calculators. The KSW Test was also given to two sections as a take home examination where the students were to monitor their own time limit and refrain from using calculators. The comparison between the two groups of students did not reflect any major differences in the Pearson Product-Moment Correlations that were calculated. Verbal interviews with both instructors that allowed the KSW Test to be taken home, completed, and returned confirmed that their discussions with their students revealed that most students did adhere to the regulations requested by the researcher. The final course examination score and the final course grade score had approximately 7% of the variance explained by the relationship between each of them and the KSW Test score (from R square = 0.069). The final course examination score and the total questionnaire score had approximately 11% of the variance explained by the relationship between each of them and the total questionnaire score (from R square = 0.107). The final course grade and the total questionnaire score had approximately 9% of the variance explained by the relationship between each of them and the total questionnaire score (from R square = 0.090). Furthermore, the following significant correlation coefficients were: (a) 0.2644 between the KSW Test score and the final course examination (b) 0.2638 between the KSW Test score and the final course grade (c) 0.3267 between the questionnaire score and the final course examination and (d) 0.3004 between the questionnaire score and the final course grade. This indicated that the total questionnaire score may be a slightly better predictor than the KSW Test score. Chapter 5/ 64 The four Fisher's Z-transformation calculations showed that there was considerable overlap among the four 9 5 % confidence intervals surrounding the four r's among the total questionnaire score, the KSW Test score, the final course examination score, and the final grade. This meant that there was no significant difference among the r's for the total KSW Test score and the total Questionnaire score in the target population and that one predictor measure was not better than the other. 5 Prediction Equations Prediction equations were developed to predict the final course examination score and the final course grade when given the questionnaire score or the KSW Test score. Equations using the bivariate multiple regression were developed to predict the final course examination score and the final course grade when given the questionnaire score and the KSW Test score. The four prediction equations were: 1 Final Grade = (0.937)(KSW Test) + 32.1 2 Final Exam = (1.53)(KSW Test) + 31.5 3 Final Grade = (0.18^(Questionnaire) + 36.3 4 Final Exam = (0.32 ^ (Questionnaire) + 36.6 Equations using the stepwise multivariate multiple regression were developed to predict the final course examination score and the final course grade when given all 22 variables (excluding gender, current help in programming, current computer access, and previous computer access). The two prediction equations were: Chapter 5/ 65 1 Final Grade = (4.68)(Expected grade) + (2A3)(Year/grade level) + (1.97)(Different programming languages) + (1.1 l)(English achievement) + (0.533) (KSW Test) + (0.121) (Questionnaire) -1.27 2 Final Exam = (6.77)(Expectedgrade) + (1.20)(KSW Test) + (0.378)(Questionnaire) - 13.1 6 Factor Analysis of All Sections Each of the five factors was composed of one or more variable used in the questionnaire. The five factors were a linear composition of some of the selected 15 variables used in the questionnaire where a weighting of 0.5 or higher was used for the selection. By examining the composition of each factor based on the variables, the following new names were interpreted for the five factors: Factor 1—Computer Literacy Factor 2--Scholastic Achievement and Language Learning Aptitude Factor ^--Mathematical Reasoning and Scholastic Achievement Factor A—Language Learning Aptitude Factor 5—Finger Dexterity These five factors below are five new independent variables: FACTOR 1 = (0.587)Computer science achievement + (0.660)Programming experience + (0.547)Expected grade + (0.508)Networking experience + (0.769)Different computer programming languages+ (0.708)Different computers Chapter 5/ 66 FACTOR 2 = (0.567)Secondary GPA + (0.644)English achievement + (0.540)Languages other than English FACTOR 3 = (0.708)iVo. of mathematics courses after secondary graduation + (0.720) Current college/university GPA FACTOR 4 = [0A99)Languages other than English FACTOR 5 = (0.527)Typ£ng achievement + [0.699)Industrial Education achievement Therefore, we can say that the above five factors had the most amount of impact on the overall score of the questionnaire (VPAIICSC). The following table summarizes these interpretations by factor number, new name, variable, and weight: TABLE X New Independent Variables Created bv the Factor Analysis Factor New Names Variables Weightings 1 Computer Literacy CPSCACHV 0.587 PRGRMEXP 0.660 EXPGRADE 0.547 NTWRKEXP 0.508 DIFLANGS 0.769 DIFCMPUT 0.708 2 Intelligence and Language SECNDGPA 0.567 Learning Aptitude ENGLACHV 0.644 FRNLGACH 0.540 3 Mathematical Reasoning PSECMATH 0.708 and Intelligence PSECDGPA 0.720 4 Language Learning Aptitude FRNLGACH 0.499 5 Finger Dexterity TYPNGACH 0.527 INDEDACH 0.699 Chapter 5/ 67 c Conclusions Pearson r's of all 20 variables correlated significantly with the final course examination score and/or the final course grade except for secondary typing achievement and secondary industrial education achievement. The three variables used in the two multivariate regression equations were expected grade, total KSW Test score, and the total questionnaire score. The expected grade had the highest B value of all three variables. The five factors extracted by the factor analysis yielded two intriguing results: 1 Factor 5 (Finger Dexterity) composed of secondary typing achievement and secondary industrial education achievement reflected significance which was not significant in the Pearson r calculations. 2 Secondary languages other than English achievement was significantly weighted twice in Factor 2 and Factor 4. This means that possibly typing and industrial education do not correlate significantly by themselves to the dependent variables but they do correlate significantly when combined together. The variable secondary languages other than English achievement seems to be a multidemensional predictor (significant weightings in Factor 2 and Factor 4). The Questionnaire developed in this study is as good as the predictive score of the KSW Test and it is faster to complete (10 minutes versus 40 minutes). d Limitations of the Study This study was limited to the study of noncognitive predictor variables which were used in the questionnaire. This study was concerned more with the prerequisite secondary subject areas and other related characteristics that would relate to learning introductory computer science courses. This means that predictor variables such as self-efficacy, personality traits, cognitive styles, and other psychological factors were Chapter 5/ 68 not used In the questionnaire nor this study. Further research in these areas is interesting and may be of value and could be attempted by other researchers interested in the prediction strengths of these variables. One other point which may be valuable is that the amount of programming language taught in each Introductory computer science course versus the amount of theoretical knowledge taught in each course was not critically analyzed. Further research comparing these two components of an introductory computer science course may give more insight into the predictability of achievement. In this study, the description given in the University of British Columbia Calendar for each course was considered as the source of information for choosing the courses to be studied. In reality, sometimes, the description given in the university calendar may not coincide with the actual curriculum taught in the classroom. Some problems that were encountered in the main study but did not show up in the pilot study were related to the design of the questionnaire. Students from other parts of Canada and other countries may not have a grade 12 secondary school system. For example, Ontario has a grade 13 secondary school system. Would one consider grade 13 as first year college or university or as the equivalent of grade 12 in British Columbia? In this study, the students were told to consider grade 13 as first year college or university. Another problem that was encountered was marking #77 on the questionnaire. Some students stated application programs such as "DBASE", "LOTUS 1 2 3", etc. were to be considered as different programming languages similar to languages such as BASIC, PASCAL, LOGO, etc. The researcher did not accept these application programs as being similar to programming languages but this issue can be debated both ways since more and more applications are becoming more complex and some skills required in Chapter 5/ 69 programming are similar to skills required to use these applications. In the future, when this questionnaire is used for predicting computer science achievement, this question should be modified to address both of these concerns (computer applications programs versus computer language prograrrirning). Another problem encountered was the readability of the names of the students on the answer sheets of the questionnaire and the KSW Test. Instead of writing 'FULL NAME' under one long blank line, it may help to clarify their precise names by having 'LAST NAME' and 'FIRST NAME' written under two short blank lines. The student numbers helped to clarify this problem but it was still time consuming. Some students that registered late did not have student numbers and other students simply did not write down their student numbers. Future use of the questionnaire should reflect this change. The last recommendation is to group the grade 12 and grade 11 subjects together on the questionnaire instead of having these subject areas grouped together according to grade. For example. Algebra 12 and Algebra 11 should be listed together (one after the other) instead of listing Algebra 12 with all the other grade 12 subjects and then listing Algebra 11 with all the other grade 11 subjects. This grouping facilitates the adding of the scores of each question under each predictor variable. For using this questionnaire as a prediction measurement, this grouping is not necessary since the instructor or teacher is only concerned about the total score. For research purposes, when one is looking at the scores of each predictor or independent variable, then it is critical. One predictor variable that was omitted from this study was the secondary subject area of Home Economics. Perhaps, this predictor variable could be examined as to its predictability in further research. It was not included since the amount of precision using machines and finger dexterity in Home Economics was considered to be less than in Industrial Education. In the factor analysis section, finger dexterity came up as an Chapter 5/ 70 important factor so it would be worthwhile to examine the achievement in Home Economics as a predictor of introductory computer science achievement. The predictive value of the KSW Test score should be used with caution since two large classes did not complete the test in class but completed it as a take-home examination. The variable gender was chosen not to be addressed by separating the groups of males and females and studying each separately. e Implications This study, its findings, and the development of the questionnaire will be valuable to teachers and counsellors in the secondary school system. This instrument (questionnaire) should be valuable and useful to teachers or instructors that teach introductory computer science courses. The score on the questionnaire may be used to predict the final course examination score and the final course grade. This score may also be then used to counsel the student/s as to their chances of succeeding in the introductory computer science course and their chances of failing. Care should be exercised in using the questionnaire for predicting introductory computer science achievement. The practical significance of the results was not large since the final course grade had approximately only 9.0% of the variance was predictable from the total Questionnaire score and the final course examination score had approximately only 10.7% of the variance was predictable from the total Questionnaire score. When the expected grade, year level, number of different programming languages, secondary English achievement, the KSW Test score, and the total Questionnaire score were used as predictors, the final course grade had approximately 29.7% of the variance was accountable. When the expected grade, the KSW Test score and the total Questionnaire score were used as predictors, the final Chapter 5/ 71 course examination score had approximately 18.2% of the variance that was accountable. This means that for practical purposes, the above combinations of variables are better predictors than any single score such as the KSW Test score or the Questionnaire score. The KSW Test and the Questionnaire were compared with each other by calculating a Pearson correlation coefficient. They had a low correlation coefficient of 0.140 (p = 0.013). This correlation is significant at p = 0.05 but not significant at p = 0.003. This shows that both of these measures are not measuring similar predictors. f Suggestions for further research I feel that a further study is needed which would use the 13 significant variables extracted in the factor analysis to compile another questionnaire. To 'fine tune' the second questionnaire, it should be statistically analyzed using similar methods and procedures as this study. From the student's perspective, I think that the completion of a questionnaire is less intimidating than a test like the KSW Test. Therefore, I think it is worthwhile investing the energy and effort to develop a strong predictive questionnaire for the target population. Scattergrams plotted by using secondary industrial education achievement and secondary typing achievement with the final course examination scores do not show any linear relationship (low or zero correlation). The scattergram plotted by using secondary languages other than English achievement does show a linear relationship (positive correlation) with the final course examination scores. These three variables had significant weightings in the factor anlaysis so they need to be explored further. Bibliography/ 72 BIBLIOGRAPHY Alspaugh, Carol Ann. 1970. 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"Equity in School Computer Use: National Data and Neglected Considerations." Journal of Educational  Computing Research 3rd ser 3: 289-311. Borg, Walter R., and Meredith Damien Gall. 1983. Educational Research An  Introduction. Ed. Nicole Benevento. 4th ed. New York: Longman Inc., 1983. Buff, Robert James. 1972. "THE PREDICTION OF ACADEMIC ACHIEVEMENT IN FORTRAN LANGUAGE PROGRAMMING COURSES." Dissertation Abstracts  International 33: 2191A. New York University. Cafolla, Ralph. 1988. "Piagetian Formal Operations and Other Cognitive Correlates of Achievement in Computer Programming." Journal of Educational Technology  Systems 1st ser 16: 45-55. Campbell, Patricia F., and George P. McCabe. 1982. Factors Relating to Persistence in a  Computer Science Major. Technical Report #82 - 15. May. Lafayette: Dept. of Statistics, Purdue University. ERIC ED 221 367. — . 1984. "Predicting the Success of Freshmen in a Computer Science Major." Communications of the Association for Computing Machinery. 11th ser 27: 1108-13. Durndell, A., H. Macleod, and G. Siann. 1987. "A Survey of Attitudes to. Knowledge about and Experience of Computers." Computers and Education 3rd ser 11:167-75. Fowler, George C., and Louis W. Glorfeld. 1981. "Predicting Aptitude in Introductory Computing: A Classification Model." Association For Educational Data  Systems Journal 2nd ser 14:96-109. Goodwin, Leonard, and John M. Wilkes. 1986. "The Psychological and Background Characteristics Influencing Students' Success in Computer Programming." Association For Educational Data Systems Journal lrst ser 20: 1-9. Bibliography/ 73 Greer, Jim. 1986. "High School Experience and University Achievement in Computer Science." Association For Educational Data Systems Journal 2nd-3rd ser 19: 216-25. Griswold, Phillip A. 1983. "Some Determinants of Computer Awareness Among Education Majors." Association For Educational Data Systems Journal 2nd ser 16: 92-103. Hilleary, Helena. 1966. A Study of the Characteristics of Students Enrolled in Business  Data Processing Classes. Los Angeles Metropolitan College. Fall. 1965. September. Los Angeles: Los Angeles Trade - Technical College. ERIC ED 010 735. Hopkins, Kenneth D., and Gene V. Glass. Basic Statistics For The Behavioral Sciences. Englewood Cliffs: Prentice - Hall Inc., 1978. Hostetler, Terry R. 1983. "Predicting Student Success in an Introductory Programming Course." Association for Computing Machinery (ACM) A Quarterly Publication  of the Special Interest Group on Computer Science Education fSIGCSE)  BULLETIN 3rd ser 15: 40-43. Jones, Jerry L. 1979. Predicting Success of Programming Students. November. Richmond: Dept. of Data Processing, J. Sargeant Reynolds Community College. ERIC ED 178 151. Kaiser, Javaid. 1982. "The Predictive Validity of GRE Aptitude Test." Paper presented  at the Annual Meeting of the Rocky Mountain Research Association at  Albuquerque. 12 November. Albuquerque. ERIC ED 226 021. Konvalina, John, Stanley Wileman, and Larry J. Stephens. 1983. "Identifying Factors Influencing Computer Science Aptitude and Achievement." Association For Educational Data Systems Journal 2nd ser. 16: 106-12. — . 1983. "Math Proficiency: A Key to Success for Computer Science Students." Communications of the Association for Computing Machinery 5th ser. 26: 377-82. Kurland, D. Midian, Roy D. Pea, Catherine Clement, and Ronald Mawby. 1986. "A Study of the Development of Programming Ability and Thinking Skills in High School Students." Journal of Educational Computing Research 4th ser. 2:429-58. Levinson, Edward M. 1986. "A Review of the Computer Aptitude, Literacy, and Interest Profile (CALIP)." Journal of Counselling and Development 10th ser 64: 658-59. Loase, John F., and Brian D. Monahan. 1983. The Relationship Between Academic  Requirements and Job Requirements In Computer Science. New York: The State University of New York at Purchase. ERIC ED 230 442. Lockheed, Marlaine E., Antonla Nielsen, and Meredith K. Stone. 1985. "Determinants of Microcomputer Literacy in High School Students." Journal of Educational  Computing Research, lrst ser 1: 81-96. Mazlack, Lawrence J. 1980. "Identifying Potential to Acquire Programming Skill." Communications of the Association for Computing Machinery, lrst ser 23: 14-17. Bibliography/ 74 Miura, Irene T. 1986. "Understanding Gender Differences in Middle School Computer Interest and Use." Paper presented at the Annual Meeting of the American  Educational Research Association (67th). 16-20 April. San Francisco. ERIC ED 273 248. Nowaczyk, Ronald H. 1983. "Cognitive Skills Needed in Computer Programming." Paper presented at the Annual Meeting of the Southeastern Psychological  Association (29th). 23-26 March. Atlanta. ERIC ED 236 466. Oman, Paul W. 1986. "Identifying Student Characteristics Influencing Success in Introductory Computer Science Courses." Association For Educational Data  Systems Journal 2nd-3rd ser 19: 226-33. Petersen, Charles G. and Trevor G. Howe. 1979. "Predicting Academic Success in Introduction to Computers." Association For Educational Data Systems  Journal 4th ser 12: 182-91. Pommersheim, John Paul. 1983. "RELATIONSHIPS BETWEEN INDIVIDUAL COGNITIVE PROFILES AND ACHIEVEMENT IN COMPUTER PROGRAMMING." Dissertation Abstracts International 45: 444A. University of Pittsburgh. Pommersheim, John P. and Frederick H. Bell. 1986. "Computer Programming Achievement, Cognitive Styles, and Cognitive Profiles." Association For  Educational Data Systems Journal lrst ser 20: 51-59. Ricardo, Catherine Mary. 1983. "IDENTIFYING STUDENT ENTERING CHARACTERISTICS DESIRABLE FOR A FIRST COURSE IN COMPUTER PROGRAMMING." Dissertation Abstracts International 44: 96A. Columbia University. Rotter, J. B. 1966. "Generalized Expectancies for Internal versus External Control of Reinforcement." Psychological Monographs lrst ser 80: (Whole No. 609). Schroeder, Mark Henry. 1978. "PIAGETIAN, MATHEMATICAL AND SPATIAL REASONING AS PREDICTORS OF SUCCESS IN COMPUTER PROGRAMMING." Dissertation Abstracts International 39: 4850A. University of Northern Colorado. Schulz, Charles E. 1984. "A Survey of Colleges and Universities regarding Entrance Requirements in Computer - related Areas." Mathematics Teacher 7th ser 77:519-21. Shoemaker, Judith S. 1986. "Predicting Cumulative and Major GPA of UCI Engineering and Computer Science Majors." Paper presented at the Annual  Meeting of the American Educational Research Association (70th). 16-20 April. San Francisco. ERIC ED 270 468. Sorge, Dennis H. and Lois K. Wark. 1984. "Factors for Success as a Computer Science Major." Association For Educational Data Systems Journal 4th ser 17: 36-45. Stephens, Larry, Stanley Wileman, John Konvalina. 1981. "Group Differences in Computer Science Aptitude." Association For Educational Data Systems  Journal 2nd ser 14: 84-95. Stephens, Larry, Stanley Wileman, John Konvalina, and Emma V. Teodoro. 1985. "Procedures for Improving Student Placement in Computer Science." Journal of  Computers in Mathematics and Science Teaching 3rd ser 4: 46-49. Bibliography/ 75 Szymczuk, Michael and Dean Frerichs. 1985. "Using Standardized Tests to Predict Achievement in an Introductory High School Computer Course." Association  For Educational Data Systems Journal lrst ser 19: 20-27. Webb, Noreen M. 1985. "Cognitive Requirements of Earning Computer Programming in Group and Individual Settings." Association For Educational Data Systems  Journal 3rd ser 18: 183-94. West, Jerry, Wendy Miller, Louis Diodato, and George H. Brown. 1985. An Analysis of  Course-Taking Patterns in Secondary Schools as Related to Student  Characteristics. High School and Beyond: A National Longitudinal Study for  the 1980's. March. Ed. The National Center for Education Statistics. Arlington: Evaluation Technologies, Inc. ERIC ED 257 633. Widmer, Connie C. and Janet Parker. 1985. "A Study of Characteristics of Student Programmers." Educational Technology 10th ser 25: 47-50. Wileman, Stanley, John Konvalina, and Larry J. Stephens. 1981. "Factors Influencing Success in Beginning Computer Science Courses." Journal of Educational  Research 4th ser. 74: 223-26. Wileman, Stanley, John Konvalina, and Larry J. Stephens. 1982. "The Relationship between Mathematical Competencies and Computer Science Aptitude and Achievement." Journal of Computers in Mathematics and Science Teaching lrst ser. 2: 20-21. Wolfe, J. M. 1977. "An interim validation report on the Wolfe Progranirning Aptitude Test." Computer Personnel 6th ser. 1-2:1-2. Appendix A/ 77 Please answer the following questions to the best of your recall ability. If you cannot remember an exact percentage or grade, mentally calculate an approximation or formulate a guess. Please fill in the blank(s) or check the appropriate box(es). Thank you for your co-operation in expanding the research in the field of Computing Studies in Education. 1 Your gender is: female male. 2 Your current age is . 3 You are currently registered in year. 4 Please write down the number of years since you have graduated from your high school (years elapsed after grade 12). For example 1, 2, 3, 4, 5, etc. Please write down the number of mathematics courses you have taken since high school graduation (after grade 12). For example 0, 1,2, 3, 4, 5, etc. Please check off your overall grade 12 average for all the courses you took. • A - 86% to 100% • B - 73% to 85% • C+ - 67% to 72% • c - 60% to 66% • P - 50% to 59% Please check off your overall college and/or university average for all the courses you took and completed after grade 12. Class 1 (80% or higher) Class 2 (65% to 79%) CD Pass (50% to 64%) [Zl Supplemental (40% to 50%) Please check off the computer science courses you have taken in school and write down the appropriate letter grade: (eg. ^ Computer Science 12 Letter Grade B ) Appendix A / 78 PROVINCIAL LETTER GRADE CLASSIFICATIONS: A - 86% to 100% B - 73% to 85% C+ - 67% to 72% C - 60% to 66% P - 50% to 59% F - below 50% 8 I I Computer Science 12 Letter Grade Computer Science 11 LetterGrade 10 Introduction to Data Processing 11 LetterGrade 11 • Computer Science 10 LetterGrade 12 Computer Science 9 LetterGrade 13 Computer Science 8 LetterGrade 14 Computer Science 7 or any computer course (K-6) LetterGrade 15 During your school career (from kindergarten to grade 12) did you program on the computers available to you at school outside normal school hours (9:00 - 12:00 and 1:00 -3:00)? For example, were you programming on the computer before school, at lunch , or after school? • Yes • No 16 If yes, approximately, how long? hours per week. 17 How many years have you been writing computer programs? years. 18 Can one of these people (father, mother, brother, sister, husband, wife, friend, or roommate) other than your instructor and teaching assistant (T.A.) write a computer program and help you when you need it? • Yes • No 19 Do you currently have access to a computer at your place of residence? IZ] Yes EZ3 No 20 Did you have access to a computer at your place of residence while you were attending junior and senior secondary school (grades 8 - 12)? • Yes • No Appendix A/ 79 21 22 Did you have access to a computer at your place of residence while you were attending elementary school (grades Kindergarten - 7)? • ves • No Check off the category or final mark/grade that you expect to get in this course: • • • • Class 1 Class 2 Pass Supplemental Examination (80% or higher) (65% to 79%) (50% to 64%) (40% to 50%) Please check off the following courses or the equivalent of the following courses you registered for in your senior secondary school grades and write down the appropriate letter grade (International Baccalaureate and honours courses will be considered similar to the courses listed below eg. IB Algebra 12 or Mathematics 12 or Honours Algebra 12 is the same as Algebra 12): (example: 713 English 12 Letter grade B J PROVINCIAL LETTER GRADE CLASSIFICATIONS: A - 86% to 100% B C+ - 67% to 72% C P - 50% to 59% F GRADE 12 BUSINESS EDUCATION 73% to 85% 60% to 66% below 50% 23 U Typing 12 Letter grade LANGUAGES 24 25 • • English 12 Foreign Languages 12 (other than English) Letter grade Average Letter grade MATHEMATICS 26 27 28 Algebra 12 Probability and Statistics 12 Geometry 12 Letter grade Letter grade Letter grade Appendix A / 80 SCIENCE 29 • Biology 12 Letter grade 30 • Chemistry 12 Letter grade 31 • Geology 12 Letter grade 32 • Physics 12 Letter grade INDUSTRIAL EDUCATION 33 • Construction 12 Letter grade 34 • Drafting 12 Letter grade 35 • Electronics 12 Letter grade 36 • Mechanics 12 Letter grade 37 • Metal 12 Letter grade 38 • Technology 12 Letter grade GRADE 11 BUSINESS EDUCATION 39 u Typing 11 Letter grade LANGUAGES 40 • English 11 Letter grade 41 • Foreign Languages (other than English) 11 Average Letter grade MATHEMATICS 42 u Algebra 11 Letter erade SCIENCE 43 • Biology 11 Letter grade 44 • Chemistry 11 Letter grade 45 • Science and Technology 11 Letter grade 46 • Physics 11 Letter grade 47 • Earth Science 11 Letter grade Appendix A/ 81 INDUSTRIAL EDUCATION 48 Construction 11 49 Drafting 11 50 Electronics 11 51 Mechanics 11 52 Metal 11 53 Technology 11 Letter grade. Letter grade _ Letter grade. Letter grade. Letter grade. 54 55 57 59 61 63 65 67 69 71 73 75 Please write down the number (0, 1, 2, 3, 4, 5, etc.) of different networking systems and different time-sharing systems that you have used in the following blank. Some examples are MTS (Michigan Terminal System), IBM T.S.O. (Time Sharing Option), Corvuus Networking System, TOPS networking system, AppleTalk (Apple Networking System), MacJanet networking system, etc. Please check off In the following box(es) the computer programming languages that you have used for programming: BASIC • • • • • • • • • • LOGO ASSEMBLY COBOL LISP MODULA PL/C ADA ALGOL SMALLTALK PROLOG 56 58 60 62 64 66 68 70 72 74 76 • • • • • • • • • • • PASCAL FORTRAN MACHINE C APL PL/I PILOT RPG SNOBOL FORTH NATAL Appendix A/ 82 Please write down other programming languages you have used for programming that are not listed above: 77 78 :  Please check off the following different computers that you have used for programming. ** NOTE ** If you have programmed on a clone of any of the following computers, please check off the original computer "FAMILY": Apple II "FAMILY" (Apple I, Apple II, Apple II+, Apple He, Apple III, Apple lie, Apple Ilgs, etc.) Apple Macintosh "Family" (Apple Lisa, Macintosh, Macintosh 512k, Macintosh Plus, Macintosh SE, Macintosh II, etc.) ATARI "FAMILY" (ATARI 520ST, ATARI 1040ST, etc.) COMMODORE "FAMILY" (200l's, PETS, C-64, C-128, etc.) COMMODORE AMIGA "FAMILY' (AMIGA AMIGA 500, AMIGA 1000, AMIGA 2000, etc.) IBM PC "FAMILY" (PC, PC-junior, XT, etc.) IBM SYSTEM 2 "FAMILY' (Models 20. 30, 50. 80, etc.) IBM SYSTEM 360 /370/ etc. Mainframes ACORN "FAMILY" (British BBC funded microcomputers) BURROUGHS ICONS (funded by the Ontario Ministry of Education) If you cannot decide on the computer "FAMILY" of the appropriate clone machine, or a machine is not listed above, then write down the name of the microcomputer/s you have used for programming here: 89 90 79 • 80 • 81 • 82 • 83 • 84 • 85 • 86 • 87 • 88 • Appendix B/ 83 APPENDIX B [PILOT STUDY AND MAIN STUDY] Computer Science Aptitude Examination INSTRUCTIONS: Place your name and student identification number in the spaces provided on the answer sheet. No calculators are allowed to be used. Please circle the correct letter corresponding to the correct answer. There is only one correct answer for each question. If you do not know the answer to a question, please leave the answer blank. When you have completed all questions, please return both the answer sheet and this booklet to the examiner/s. TIME LIMIT: 40 MINUTES. DO NOT WRITE IN THIS BOOKLET! DO NOT WRITE IN THIS BOOKLET! DO NOT WRITE IN THIS BOOKLET! PAPER FOR ROUGH CALCULATIONS IS GIVEN! Copyright 1983 by J. Konvalina, L. Stephens and S. Wileman Department of Mathematics and Computer Science University of Nebraska at Omaha 68182 Appendix B/ 84 PART I fSequences and Logic) For questions 1-4 look for a pattern and fill in the missing term in the sequence: AC,F,HK,M, a) N b) O 0 P d) 9 e) R ABC, ABD, ABE, ACD, ACE, a) ADE b) ACB c) AED d) ADC e) AEC 1, 3, 4 6 2. 5 4, 7 3. 6 a) 1 2 b) 3 8 c) 4 5 d) 5 8 e) 2 5 1, 1, 2, 3, 5, 8, a) 13 b) 8 0 11 d) 9 e) 40 How many numbers are there in the sequence below if all the missing terms (indicated by ...) are included? 0, 3.6.9, 12, 15,.... 240 a) 240 b) 241 c) 80 d) 81 e) none of these A teacher said to a student, "If you receive an A on the final exam, then you will pass the course." Suppose the student did not pass the course. What conclusion is valid? a) The student received an A on the final exam. b) The student did not receive an A on the final exam. c) The student flunked the final exam. d) If the student passed the course, then he or she received an A on the final. e) None of these Is valid. Appendix B/ 85 John said to Jane, "If it rains, then I won't play tennis." Suppose it did not rain, then what conclusion is valid? a) John played tennis. b) John did not play tennis. c) If John does not play tennis, then it rains. d) It did not rain and John played tennis. e) None of these is valid. Suppose all computers are logical devices, and some computers are bistable. What conclusion is valid? a) All logical devices are bistable. b) All computers are bistable. c) Some computers are not logical devices. d) Some logical devices are computers. e) None of these is valid. Think of a number. Add 3 to the number. Multiply your answer by 2. Subtract 4 from your answer. Divide by 2. Subtract the number with which you started. Your answer is: a) 0 b) 1 c) 2 d) negative e) none of these Which one of the words does not belong to the group? a) REDDER b) BETTER c) RADAR d) PEEP e) POP Appendix B / 86 PART II (Calculator Simulator) Consider the calculator below with a 4 - digit display, digits 0 through 9, and operations A B, C, D, E, and R. D I S P L A Y / 0 /° 0 1 2 A 3 4 5 B 6 7 8 C 9 R E D KEYBOARD The meaning of the operations of the calculator are as follows: R = Reset the display so that all digits are zero. E = Enter the number pressed after the letter E into the display. A = Add the number pressed after the letter A to the number in the display and display the result (sum). B = Subtract the number in the display from the number pressed after the letter B and display the result (difference). C = Multiply the number pressed after the letter C by the number in the display and display the result (product). D = Divide the number pressed after the letter D into the number in the display and display the result (whole number quotient). Note: Except for the letter R a number is pressed after a letter. An example of a calculator program is the following (Instructions are performed from left to right in order): RE20B50D6 This program first resets the display to zero, then enters the number 20 into the display, subtracts 20 from 50 (display now reads 30), and finally divides the result in the display by 6. The display reads 5 after the last operation. Now answer the following questions based on the calculator above: Appendix B/ 87 11 A student scored 85, 66, and 92 on three exams. Which calculator program will display the average of the three exams after the last operation? a) RE85A66B92D3 b) RE85A66D3A92 c) RE92A66A85D3 d) RE92A66A85C3 e) None of these 12 Mrs. Gross bought 4 single grocery items at the following prices: 69 cents, 45 cents, 12 cents, 37 cents. Also, she bought 5 kilograms of bananas at 39 cents a kilogram. She paid for the groceries with a $10 bill (1000 cents). Which calculator program will display her correct change (in cents)? a) RE69A45A12A39B1000 b) RE39C5A69A45A12B1000 c) RE69A45A12A37A39C5B1000 d) RE5C39A69A45A12A37B1000 e) None of these 13 Find the number displayed after the last operation of the following calculator program: RE6A0B8C2C4D16B1 a) 2 b) 0 c) 1 d) a negative number e) None of these 14 Which statement best describes the following calculator program: RE13D3C3B13 a) Divides two numbers, 13 and 3, and displays the quotient. b) Divides, multiplies the result, and finally displays 13 again. c) Computes and finally displays a negative number. d) Computes and displays 3 times 13 minus 13. e) Computes and displays the remainder when 13 is divided by 3. 15 What last operation must be added to the following program so that the display will read 1 after the last operation? RE12A3C2B56D2 ? a) D14 b) Al c) B13 d) B12 e) None of these Appendix B / 88 PART III (Algorithml Assume we have four light bulbs arranged In a circle labelled 1, 2, 3, and 4 as shown In the figure below. Assume further that we have four switches connected so that each switch controls the light bulb with the corresponding number. Consider the following set of instructions, but do not perform the actions indicated yet. 1 Turn on the light bulb that Is directly across from the single light bulb that is on. 2 If any odd - numbered light bulb is on, go to step 4. 3 Turn off the lowest - numbered light bulb, and go to step 5. 4 Turn off the highest - numbered light bulb. 5 Turn on the bulb next to the highest - numbered bulb that is on, in the clockwise direction. 6 Turn off any even - numbered bulbs which might be on, and stop. Appendix B/ 89 Now answer the following questions: Assume only light bulb # 1 is on. Perform the instructions, starting with step 1. When you stop in step 6 a) Light bulbs #3 and #4 are on. b) No light bulbs are on. c) Only light bulb # 1 is on. d) Only light bulb #2 is on. e) None of the above Perform the instructions again. This time assume only light bulb #2 is on at the beginning. When you stop in step 6 a) Only light bulb #1 is on. b) Light bulbs #2 and #3 are on. c) At least three light bulbs are on. d) Only two light bulbs are on. e) None of the above Again perform the instructions, this time assuming only light bulb #3 is on initially. When you stop in step 6 a) Only bulb #2 is on. b) Only bulb #3 is on. c) Only bulb #4 is on. d) All bulbs will be on. e) None of the above Finally, perform the instructions assuming only bulb #4 was initially on. When you stop in step 6 a) Light bulbs #2 and #4 are on. b) Light bulbs #1 and #3 are on. c) At least one even numbered bulb will be on. d) At least one odd numbered bulb will be on. e) None of the above Appendix B/ 90 20 Based on your experience in performing the instructions, a) The instructions can be applied regardless of the number of light bulbs initially turned on. b) Regardless of which light bulb was initially on, when we stop in step 6 all light bulbs will be off. c) Regardless of which light bulb was initially on, when we stop in step 6 only light bulb #1 will be on. d) When an even - numbered bulb is initially turned on, then when we stop in step 6 only light bulb #3 will be on. e) None of the above PART IV fWord Problems) 21 Six times a number is three more than twice the number. What is the number? a) 4 b) .3 c) 3 d) -4I e) None of these 3 4 4 2 22 A bank contains nickels and dimes. The total value of the coins is $2.10 and there are 3 more dimes than nickels. How many nickels are there? a) 13 b) 12 c) 15 d) 36 e) None of these 23 A law requires that the amount of chicken used in hot dogs cannot exceed 25% of the total weight of the hot dog. How many grams could a hot dog weigh if it contains 1.5 grams of chicken? a) 3 b) 5 c) 5.5 d) 6 e) None of these 24 A farmer mixes seed worth 15 cents per kilogram with seed worth 20 cents per kilogram to produce a mixture of 50 kilograms of seed worth 18 cents per kilogram. How many kilograms of seed worth 20 cents per kilogram did he use in the mixture? a) 20 b) 25 c) 30 d) 40 e) None of these 25 Volumes 12 through 29 of an encyclopedia have misprints on pages 21 through 53 of each volume. How many pages In the encyclopedia have misprints? a) 18 b) 32 c) 544 d) 594 e) None of these PLEASE CHECK ALL YOUR CALCULATIONS!! Appendix C / 91 APPENDIX C ANSWER SHEET [PILOT STUDY AND MAIN STUDY] Answer Sheet for Computer Science Aptitude Examination Full Name U.B.C. Student Number PLEASE USE A PENCIL TO CIRCLE THE CORRECT ANSWER! 1 a b c d e 2 a b c d e 3 a b c d e 4 a b c d e 5 a b c d e 6 a b c d e 7 a b c d e 8 a b c d e 9 a b c d e 10 a b c d e 11 a b c d e 12 a b c d e 13 a b c d e 14 a b c d e 15 a b c d e 16 a b c d e 17 a b c d e 18 a b c d e 19 a b c d e 20 a b c d e 21 a b e d e 22 a b c d e 23 a b c d e 24 a b c d e 25 a b c d e Appendix D/ 92 APPENDIX D ANSWER KEY [PILOT STUDY AND MAIN STUDY] Answer Key for Computer Science Aptitude Examination (KSW Test) THE CORRECT ANSWERS ARE GIVEN BELOW fFROM DR. STEPHENS'S LETTER DATED NOVEMBER 30. 19871 1 c 2 a 3 d 4 a 5 d 6 b 7 e 8 d 9 b 10 b 11 c 12 d 13 b 14 e 15 e 16 c 17 a 18 e 19 d 20 c 21 c 22 b 23 d 24 c 25 d Appendix E / 93 APPENDIX E INSTRUCTIONS [MAIN STUDY]  Directions for Questionnaire and _ _ Examination  1 DO NOT WRITE ON THESE BOOKLETS!! THEY WILL BE USED AGAIN. 2 MAKE SURE YOUR BOOKLET HAS 7 PAGES AND AN ANSWER SHEET. 3 WRITE YOUR NAME AND STUDENT NUMBER ON THE ANSWER SHEET ONLY. 4 CIRCLE THE CORRECT LETTERS OR FILL IN THE BLANKS ON THE ANSWER SHEET WITH A PENCIL OR PEN. 5 WHEN YOU FINISH, HAND IN THE BOOKLET AND THE ANSWER SHEET INTO THE BOX OR THE EXAMINER/S (MAKE SURE THAT YOUR NAME AND STUDENT  NUMBER ARE ON THE ANSWER SHEET). 6 TAKE ONE "TAKE HOME EXAMINATION" FROM THE CORRECT BOX OR THE EXAMINER/S. IT WILL HAVE 8 PAGES AND AN ANSWER SHEET. 7 ALLOW YOURSELF ONLY 40 MINUTES FOR THIS EXAM AND DO NOT USE A CALCULATOR. 8 RETURN 'TAKE HOME EXAMINATION" BOOKLET AND ANSWER SHEET WITH YOUR FIRST ASSIGNMENT FOR 10 MARKS ON SEPTEMBER 23, 1988. Appendix F/ 95 Please answer the following questions to the best of your recall ability. If you cannot remember an exact percentage or grade, mentally calculate an approximation or formulate a guess. Please fill in the blank(s) or circle the correct letter on the answer sheet in this booklet Thank you for your co-operation in expanding the research in the field of Computing Studies in Education. 1 Your gender (sex) is: a) female b) male 2 Please write down your current age on the answer sheet. 3 Please write down your current U.B.C. official registration year on the answer sheet. (For example: 1st year, 2nd year, 3rd year, etc). 4 Please write down the number of years since you have graduated from your high school on the answer sheet (years elapsed after grade 12). For example 0, 1,2, 3, 4, 5, etc. 5 Please write down the number of mathematics courses you have taken since high school graduation on the answer sheet (after grade 12). For example 0, 1,2, 3, 4, 5, etc. 6 Please circle your overall grade 12 average for all the courses you took. a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) 7 Please circle your overall college and/or university average for all the courses you took and completed after grade 12 or circle e) if you did not take any. a) Class 1 (80% or higher) b) Class 2 (65% to 79%) c) Pass (50% to 64%) d) Supplemental (40% to 50%) e) Have not taken anv courses after grade 12 Please circle the letter grade you obtained in each of the following computer science courses you may have taken in high school on the answer sheet. Note: Please leave all the courses you did not take blank. 8 Computer Science 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 9 Computer Science 11 a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) f) below 50% (F) 10 Introduction to Data Processing 11 a) 86% to 100% (A) b) 73% to 85% (B) d) 60% to 66% (C) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix F/ 96 11 Computer Science 10 a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) f) below 50% (F) 12 Computer Science 9 a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) f) below 50% (F) 13 Computer Science 8 a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) f) below 50% (F) 14 Computer Science 7 and any computer course (Kindergarten-Grade 6) a) 86% to 100% (A) b) 73% to 85% (B) c) 67% to 72% (C+) d) 60% to 66% (C) e) 50% to 59% (P) f) below 50% (F) 15 During your school career (from kindergarten to grade 12) did you program on the computers available to vou at school outside normal school hours (9:00 - 12:00 and 1:00 -3:00)? For example, were you programming on the computer before school, at lunch , or after school? a) Yes b) No 16 If ves. write down the number of hours ner week on the answer sheet. 17 Write down the number of vears that vou have been writing computer programs on the answer sheet. 18 Can one of these people (father, mother, brother, sister, husband, wife, friend, or roommate) other than your instructor and teaching assistant (T.A.) write a computer program and help you when you need it? a) Yes b) No 19 Do you currently have access to a computer at your place of residence? a) Yes b) No 20 Did you have access to a computer at your place of residence while you were attending junior and senior secondary school (grades 8 - 12)? a) Yes b) No 21 Did you have access to a computer at your place of residence while you were attending elementary school (grades Kindergarten - 7)? a) Yes b) No 22 Circle the category or final mark/grade that you expect to get in this course: a) Class 1 (80% or higher) b) Class 2 (65% to 79%) c) Pass (50% to 64%) d) Supplemental (40% to 50%) PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix F/ 97 Please circle the appropriate letter grade of the following courses on the answer sheet or the equivalent of the following courses you registered for in your senior secondary school grades. (International Baccalaureate and honours courses will be considered similar to the courses listed below. For example, IB Algebra 12 or Mathematics 12 or Honours Algebra 12 Is the same as Algebra 12). GRADE 12 BUSINESS EDUCATION 23 Typing 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) LANGUAGES 24 English 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 25 Foreign Languages 12 (other a) 86% to 100% (A) d) 60% to 66% (C) than English) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) MATHEMATICS 26 Algebra 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 27 Probability and Statistics 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 28 Geometry 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) S C I E N C E 29 Biology 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 30 Chemistry 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 31 Geology 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 32 Physics 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix F / 98 INDUSTRIAL EDUCATION 33 Construction 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 34 Drafting 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 35 Electronics 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 36 Mechanics 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 37 Metal 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 38 Technology 12 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) GRADE 11 BUSINESS EDUCATION 39 Typing 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) LANGUAGES 40 English 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) 41 Foreign Languages (other than English) 11 a) 86% to 100% (A) b) 73% to 85% (B) d) 60% to 66% (C) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) MATHEMATICS 42 Algebra 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) S C I E N C E 43 Biology 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73"/o to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) 44 Chemistry 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix F / 99 4S Science and Technology 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 46 Physics 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) f) below 50% (F) 47 Earth Science 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) INDUSTRIAL EDUCATION 48 Construction 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) 49 Drafting 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) 50 Electronics 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) Si Mechanics 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) S2 Metal 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) S3 Technology 11 a) 86% to 100% (A) d) 60% to 66% (C) b) 73% to 85% (B) e) 50% to 59% (P) c) 67% to 72% (C+) fl below 50% (F) 54 Please write down the number (0, 1, 2, 3, 4, 5, etc.) of different networking systems and different time-sharing systems that you have used on the answer sheet. Some examples are MTS (Michigan Terminal System), IBM T.S.O. (Time Sharing Option), Corvuus Networking System, TOPS networking system, AppleTalk (Apple Networking System), MacJanet networking system, etc. Please circle a) on the answer sheet for the computer programming languages that you have used for programming for numbers 55 to 75 below. Languages you have not used, you may circle b) or leave blank. SS BASIC a) Yes b) No 58 PASCAL a) Yes b) No 56 LOGO a) Yes b) No 59 FORTRAN a) Yes b) No S7 ASSEMBLY a) Yes b) No 60 MACHINE . a) Yes b) No PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix F/ 100 6i COBOL a) Yes b)No 69 C a) Yes b) No 62 LISP a) Yes b) No 70 APL a) Yes b) No 63 MODULA a) Yes b) No 71 PL/I a) Yes b) No 64 PL/C a) Yes b) No 72 PILOT a) Yes b) No 65 ADA a) Yes b) No 73 RPG a) Yes b) No 66 ALGOL a) Yes b) No 74 SNOBOL a) Yes b) No 67 SMALLTALK a) Yes b) No 75 FORTH a) Yes b) No 68 PROLOG a) Yes b) No 76 NATAL a) Yes b) No 77 Please write down on the answer sheet all other programming languages you have used for programming that are not listed above. Please circle a) for the following different computers that you have used for programming on the answer sheet. Computers you have not used, you may circle b) or leave blank. ** NOTE ** If vou have programmed on a clone or copv of anv of the following computers. please circle the original computer "FAMILY". 78 Apple II "FAMILY" (Apple I, Apple II, Apple II+, Apple He, Apple III, Apple He, Apple Ilgs, etc.) a) Yes b) No 79 Apple Macintosh "Family" (Apple Lisa, Macintosh, Macintosh 512k, Macintosh Plus, Macintosh SE, Macintosh II, etc.) a) Yes b) No 80 ATARI "FAMILY" (ATARI 520ST, ATARI 1040ST, etc.) a) Yes b) No 81 COMMODORE "FAMILY' (200l's, PETS, C-64, C-128, etc.) a) Yes b) No 82 COMMODORE AMIGA "FAMILY" (AMIGA, AMIGA 500, AMIGA 1000, AMIGA 2000, etc.) a)Yes b) No 83 IBM PC "FAMILY" (PC, PC-junior, XT, etc.) a) Yes b) No 84 IBM SYSTEM 2 "FAMILY" (Models 20, 30, 50, 80, etc.) a) Yes b) No 85 IBM SYSTEM 360 /370/ etc. Mainframes a) Yes b) No 86 ACORN "FAMILY" (British BBC funded microcomputers) a) Yes b) No 87 BURROUGHS ICONS (funded by the Ontario Ministry of Education) a) Yes b) No 88 If you cannot decide on the computer "FAMILY" of the appropriate clone or copy machine. or a machine is not listed above, then write down the namefs) of the microcomputer(s) you have used for programming on the answer sheet. PLEASE DO NOT WRITE IN THIS BOOKLET! Appendix G / 101 APPENDIX G ANSWER SHEET [MAIN STUDY] Answer Sheet For Variables Predicting Achievement In Introductory Computer Science Courses  Full Name U.B.C. Student Number PLEASE USE A PENCIL TO CIRCLE THE CORRECT ANSWER OR FILL IN THE BLANK(S)!  1 a b 23 a b c d e f 2 vears old. 24 a b c d e f 3 vear. 25 a b c d e f 4 vearfs). 26 a b c d e f 5 27 a b c d e f 6 a b c d e 28 a b c d e f 7 a b c d e 29 a b c d e f 8 a b c d e f 30 a b c d e f 9 a b c d e f 31 a b c d e f 10 a b c d e f 32 a b c d e f 11 a b c d e f 33 a b c d e f 12 a b c d e f 34 a b c d e f 13 a b c d e f 35 a b c d e f 14 a b c d e f 36 a b c d e f 15 a b 37 a b c d e f 16 hours Der week. 38 a b c d e f 17 vears. 39 a b c d e f 18 a b 40 a b c d e f 19 a b 41 a b c d e f 20 a b 42 a b c d e f 21 a b 43 a b c d e f 22 a b c d 44 a b c d e f 45 a b c d e f 74 46 a b c d e f 75 47 a b c d e f . 76 48 a b c d e f 77 49 a b c d e f 78 50 a b c d e f 79 S l a b c d e f 80 52 a b c d e f 81 53 a b c d e f 82 54 83 55 a b 84 56 a b 85 57 a b 86 58 a b 87 59 a b 88 60 a b 61 a b 62 a b 63 a b 64 a b 65 a b 66 a b 67 a b 68 a b 69 a b 70 a b 71 a b 72 a b 73 a b Appendix G / 102 a b a b a b a b a b a b a b a b a b a b a b a b a b Appendix H / 103 APPENDIX H MARKING KEY [PILOT STUDY AND MAIN STUDY] Marking Key For Measure 'Variables Predicting Achievement In Introductory Computer Science Courses"  l 2 3 4 5 6 8 - 14 15 16 - 17 18 19 20 - 21 22 23 & 39 GENDER01 CURRTAGE YEARLEVL YSECGRAD PSECMATH SECNDGPA PSECDGPA CPSCACHV PRGRMEXP CURRHPRG CURRACCS PREVACCS EXPGRADE TYPNGACH a = 0(F); b = 1(M) value in blank value in blank value in blank value in blank a = 5, b = 4, c = 3, d = 2, e = 1 a = 4, b = 3, c = 2, d = 1, e = 0 a = 5, b = 4, c = 3, d = 2, e = 1, f=0 a = 1, b = 0 value in blank a = 1, b = 0 a = 1, b = 0 a = 1, b = 0 a = 4, b = 3, c = 2, d = 1 a = 5, b = 4, c = 3, d = 2, e = l , f=0 Appendix H / 104 24 & 40 ENGLACHV a = 5, b = 4, c = 3, d = 2, e = 1, f=0 25 & 41 FRNLGACH a = 5, b = 4, c = 3, d = 2, e = 1, f=0 26 - 28 & 42 MATHACHV a = 5, b = 4, c = 3, d = 2 ,e= l , f=0 29 - 32 & 43 - 47 SCNCACHV a = 5, b = 4, c = 3, d = 2, e = 1, f=0 33 - 38 & 48 - 53 INDEDACH a = 5, b = 4, c = 3, d = 2, e = 1, f = 0 54 55 - 76 & 77 NTWRKEXP DIFLANGS value in blank a = 1; b = 0 1 for each language 78 - 87 & 88 DIFCMPUT a = 1; b = 0 1 for each computer Total Score VPAIICSC Sum of scores for each question except #1 - #4 Appendix 1/ 105 APPENDIX I [MAIN STUDY] Pearson Product-Moment Correlation Coefficients Among All Variables Pearson product-moment correlation coefficients were calculated among all of the 22 variables listed in Appendix J (Gender, Current Help in Programming, Current Computer Access, and Previous Computer Access were excluded since they were nominal variables), total scores on the KSW Test and the Questlonniare, and the two dependent variables (final course examination score and final course grade). The numbers 1 to 22 along the horizontal and vertical labels of the table were used to designate the 22 variables listed below, respectively, for the 22 by 22 matrix: 1 Current Age 2 Year/Grade Level 3 Number of Years Elapsed after High School/Secondary Graduation 4 Number of Mathematics Courses Completed after Grade 12 5 Overall Grade 12 Percentage/Grade Point Average 6 Current College/University Overall Percentage/Grade Point Average 7 Prior Computer Science Achievement 8 Prior Computer Programming Experience 9 Expected Grade of Current Computer Course 10 Typing Achievement—Business Education 11 Secondary English Achievement 12 Secondary Languages Other Than English 13 Secondary Mathematics Achievement 14 Secondary Science Achievement 15 Secondary Industrial Education Achievement 16 Number of Time-Sharing and Networking Systems Previously Used 17 Number of Different Programming Languages Used for Programming 18 Number of Different Types of Computers Previously Used for Programming 19 Score on KSW Test 20 Score on Questionnaire 21 Score on final course examination 22 Final course grade The table of Pearson product-moment correlation coefficients below was divided into two parts (22 by 11 and 22 by 11) due to space restrictions: Appendix 1/ 106 TABLE XI Pearson r's Among All 22 Variables Vars. 1 2 3 4 5 6 7 8 9 10 11 1.00 .610* .967* .273* -.322* .399* -.264* -.067* -.024 .031 -0.234* (661) (661) (653) (661) (661) (661) (661) (661) (659) (661) (661) .001 .001 .001 .001 .001 .001 .044 .272 .210 .001 2 .610* 1.00 .617* .448* -.142 .570 -.245 -.154 -.073 .083 -.028 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .001 .001 .001 .001 .001 .001 .031 .017 .237 3 .967* .617* 1.00 .304* -.266* .442* -.258* -.075* -.035 .063 -.174* (653) (654) (654) (654) (654) (654) (654) (654) (652) (654) (654) p< .001 .001 .001 .001 .001 .001 .028 .186 .055 .001 4 .273* .448* .304* 1.00 -.008 .569* -.078* .035 .052 .038 -.168* (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .001 .001 .419 .001 .022 .185 .089 .163 .001 5 -.322* -.142* -.266* -.008 1.00 -.027 .221* .125* .206* -.009 .413* (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .001 .001 .419 .245 .001 .001 .001 .409 .001 6 .399* .570* .442* .569* -.027 1.00 -.177* -.062 .035 .067* -.017 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .001 .001 .001 .245 .001 .056 .184 .042 .334 7 -.264* -.245* -.258* -.078* .221* -.177* 1.00 .514* .215* .013 .155* (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .001 .001 .022 .001 .001 .001 .001 .367 .001 8 -.067* -.154* -.075* .035 .125* -.062 .514* 1.00 .267* .001 .038 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .044 .001 .028 .185 .001 .056 .001 .001 .486 .166 Appendix 1/ 107 Vars. 1 2 3 4 5 6 7 8 9 10 11 9 -.024 -.073 -.035 .052 .206 (659) (660) (652) (660) (660) p< .272 .031 .186 .089 .001 .035 .215 .267 1.00 -.080 .049 (660) (660) (660) (660) (660) (660) .184 .001 .001 .020 .105 10 .031 .083 .063 .038 -.009 (661) (662) (654) (662) (662) p< .210 .017 .055 .163 .409 .067 .013 .001 -.080 1.00 .034 (662) (662) (662) (660) (662) (662) .042 .367 .486 .020 .194 11 -.234 -.028 -.174 -.168 .413 -.017 .155 .038 .049 .034 1.00 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .237 .001 .001 .001 .334 .001 .166 .105 .194 12 -.124* -.009 -.083* -.103* .220* -.009 .125* .019 .037 .067* .395* (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .407 .017 .004 .001 .405 .001 .310 .174 .043 .001 13 -.178* -.117* -.159* .109* .430* (661) (662) (654) (662) (662) p< .001 .001 .001 .003 .001 .002 (662) .476 .101* (662) .005 .071 (662) .034 .228 (660) .001 .011 (662) .386 .157* (662) .001 14 -.162 -.045 -.118 .179 .393 .019 .069 .154 .226 -.014 .203 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .001 .125 .001 .001 .001 .309 .037 .001 .001 .361 .001 15 .077 .024 .090 .132 -.050 .017 .033 .264 .071 -.007 -.061 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .024 .268 .010 .001 .099 .328 .198 .001 .034 .425 .058 16 .052 .099 .030 .221 .067 .143 .152 .252 .141 .010 .008 (661) (662) (654) (662) (662) (662) (662) (662) (660) (662) (662) p< .092 .006 .223 .001 .043 .001 .001 .001 .001 .403 .415 17 -.026 -.042 -.058 .238 .183 .035 .397 (661) (662) (654) (662) (662) (662) (662) p< .250 .138 .070 .001 .001 .188 .001 .489* .347* (662) (660) .001 .001 -.080 -.012 (662) (662) .020 .382 18 -.117* -.136* -.154* .095* .202* (661) (662) (654) (662) (662) p< .001 .001 .001 .007 .001 -.023 (662) .276 .342 (662) .001 .396* .316* (662) (660) .001 .001 -.017 .059 (662) (662) .328 .064 Appendix 1/ 108 Vars. 1 2 3 4 5 6 7 8 9 10 11 19 .120 .036 .163 .060 .259 .146 -.006 -.053 .213 -.063 .136 (254) (254) (250) (254) (254) (254) (254) (254) (254) (254) (254) p< .029 .282 .005 .170 .001 .010 .463 .201 .001 .160 .015 20 -.159* -.078* -.118* .262* .460* .085* .554* .668* .385* .114* .301* (659) (660) (652) (660) (660) (660) (660) (660) (658) (660) (660) p< .001 .022 .001 .001 .001 .015 .001 .001 .001 .002 .001 21 -.043 .008 .015 .152 .280 .174 .178 .204 .298 -.054 .134 (605) (606) (598) (606) (606) (606) (606) (606) (606) (606) (606) p< .144 .415 .362 .001 .001 .001 .001 .001 .001 .092 .001 22 .112 .189 .114 .186 (615) (616) (608) (616) p< .003 .001 .002 .001 .256 (616) .001 .211* (616) .001 .128 (616) .001 .196 (616) .001 .299 (616) .001 -.063 (616) .059 .132 (616) .001 Appendix 1/ 109 Vars. 12 13 14 15 16 17 18 19 20 21 22 1 * -.124 (661) .001 -.178* (661) .001 -.162* (661) .001 .077* (661) .024 .052 (661) .092 2 -.009 (662) .407 -.117* (662) .001 -.045 (662) .125 .024 (662) .268 .099* (662) .006 3 -.083* (654) .017 -.159* (654) .001 -.118* (654) .001 -.090* (654) .010 .030 (654) .223 4 * -.103 (662) .004 .109* (662) .003 * .180 (662) .001 .132* (662) .001 .221* (662) .001 5 .220* (662) .001 .430* (662) .001 .393 (662) .419 -.050 (662) .099 .067* (662) .043 6 -.009 (662) .405 .002 (662) .476 .019 (662) .309 .017 (662) .328 .143* (662) .001 7 .125* (662) .001 .101* (662) .005 * .069 (662) .037 .033 (662) .198 .152* (662) .001 8 .019 (662) .310 .071* (662) .034 .154* (662) .001 * .264 (662) .001 .252* (662) .001 9 .037 (660) .174 .228* (660) .001 .226* (660) .001 .071* (660) .034 .141* (660) .001 10 .067* (662) .043 .011 (662) .386 -.014 (662) .361 -.007 (662) .425 .010 (662) .403 -.026 (661) .250 -.117* (661) .001 .120* (254) .029 -.159 (659) .001 -.043 (605) .144 .112* (615) .003 -.042 (662) .138 * -.136 (662) .001 .036 (254) .282 -.078* (660) .022 -.009 (606) .415 -.189* (616) .001 -.058 (654) .070 -.154* (654) .001 .163* (250) .005 -.118* (652) .001 .015 (598) .362 .114* (608) .002 .238* (662) .001 .095* (662) .007 .060 (254) .170 .262* (660) .001 .152* (606) .001 .186* (616) .001 .183* (662) .001 .202* (662) .001 .260* (254) .001 .460* (660) .001 .280* (606) .001 .256* (616) .001 .035 (662) .188 -.023 (662) .276 .146* (254) .010 .085* (660) .015 .174 (606) .001 .211* (616) .001 .397* (662) .001 .342* (662) .001 -.006 (254) .463 .554* (660) .001 .178* (606) .001 .128* (616) .001 .489* (662) .001 .396* (662) .001 -.053 (254) .201 .668* (660) .001 .204* (606) .001 .196* (616) .001 .347* (660) .001 .316* (660) .001 .213* (254) .001 .385* (658) .001 .298* (606) .001 .299* (616) .001 -.080* (662) .020 -.017 (662) .328 -.063 (254) .160 .114* (660) .002 -.054 (606) .092 -.063 (616) .059 Appendix 1/ 110 Vars. 12 13 14 15 16 17 18 19 20 21 22 11 .395 .157 .203 -.061 .008 -.012 .059 (662) (662) (662) (662) (662) (662) (662) p< .001 .001 .001 .058 .415 .382 .064 .136 .301 .134 .132 (254) (660) (606) (616) .015 .001 .001 .001 12 1.00 .170 .040 -.095 .013 -.045 -.016 (662) (662) (662) (662) (662) (662) (662) p< .001 .154 .007 .370 .122 .345 .121 .252 .092 .069 (254) (660) (606) (616) .028 .001 .012 .044 13 .170 1.00 .462 -.026 .061 (662) (662) (662) (662) (662) p< .001 .001 .252 .057 .108 .115 .086 .488 .174 .159 (662) (662) (254) (660) (606) (616) .003 .002 .086 .001 .001 .001 14 .040 .462 1.00 .085 .080 (662) (662) (662) (662) (662) p< .154 .001 .015 .020 .110 .110 .111 .609 .215 .181 (662) (662) (254) (660) (606) (616) .002 .002 .039 .001 .001 .001 15 -.095 -.026 .085 1.00 .054 .157 .053 -.011 (662) (662) (662) (662) (662) (662) (662) (254) p< .007 .252 .015 .085 .001 .086 .430 .387 -.028 (660) (606) .001 .250 -.032 (616) .214 16 .013 (662) p< .370 .061 (662) .057 .080 (662) .020 .054 (662) .085 1.00 (662) .468 (662) .001 .382 (662) .001 .012 (254) .423 .328* .122* (660) (606) .001 .001 .170 (616) .001 17 -.045 (662) p< .122 .108 (662) .003 .110* (662) .002 .157* (662) .001 .468 (662) .001 1.00 (669) .654 (669) .001 .029 (261) .319 .524 (660) .001 .268 (613) .001 .287 (623) .001 18 -.016 (662) p< .345 .115* (662) .002 .110* (662) .002 .053 (662) .086 .382 (662) .001 .654* (669) .001 1.00 (669) -.001 (261) .491 .459 (660) .001 .161* (613) .001 .182 (623) .001 19 .121 (254) p< .028 .086 (254) .086 .111* (254) .039 -.011 .012 (254) (254) .430 .423 .029 -.001 (261) (261) .319 .491 1.00 (265) .140 (252) .013 .264 (246) .001 .264 (252) .001 20 .252 (660) p< .001 .488 (660) .001 .609 (660) .001 .387 .328 (660) (660) .001 .001 .524 .459 (660) (660) .001 .001 .140 (252) .013 1.00 (660) .327 (605) .001 .300 (615) .001 Appendix 1/ 1 1 1 Vars. 12 13 14 15 16 17 18 19 20 21 22 21 .092 .174 .215 -.028 .122 .268 .161 .264 .327 1.00 .868 (606) (606) (606) (606) (606) (613) (613) (246) (605) (616) (616) p< .012 .001 .001 .250 .001 .001 .001 .001 .001 .001 22 .069 .159 .181 -.032 .170 .287 .182 .264 .300 .868 1.00 (616) (616) (616) (616) (616) (623) (623) (252) (615) (616) (626) p< .044 .001 .001 .214 .001 .001 .001 .001 .001 .001 *p < 0.05 Appendix J / 112 APPENDIX J [PILOT STUDY AND MAIN STUDY] Twenty-two Variables Used in The Study 1 Gender 2 Current Age 3 Year/Grade Level 4 Number of Years Elapsed after High School/Secondary Graduation 5 Number of Mathematics Courses Completed after Grade 12 6 Overall Grade 12 Percentage/Grade Point Average 7 Current College/University Overall Percentage/Grade Point Average 8 Prior Computer Science Achievement 9 Prior Computer Programming Experience 10 Current Help in Programming 11 Current Computer Access 12 Previous Computer Access 13 Expected Grade of Current Computer Course 14 Typing Achievement—Business Education 15 Secondary English Achievement 16 Secondary Languages Other Than English Appendix J / 113 17 Secondary Mathematics Achievement 18 Secondary Science Achievement 19 Secondary Industrial Education Achievement 20 Number of Time-Sharing and Networking Systems Previously Used 21 Number of Different Programming Languages Used for Programming 22 Number of Different Types of Computers Previously Used for Programming Literature Index/ 114 LITERATURE INDEX Alspaugh (1970, 1972) 6 Alspaugh (1971) 16 Bauer et al (1968) 5 Becker and Sterling (1987) 21 Buff (1972) 6 Cafolla(1988) 15 Campbell and McCabe (1982, 1984) 10 Durndell et al (1987) 15 Fowler and Glorfeld (1981) 8 Goodwin and Wilkes (1986) 14 Greer (1986) 12 Griswold (1983) 13 Hilleary (1966) 12 Hostetler (1983) 9 Jones (1979) 12 Konvalina et al (1983) 10 Kurland etai (1986) 17 Literature Index/ 115 Lockheed et al (1985) 16 Miura (1986) 21 Nowaczyk (1983) 10 Oman (1986) 14 Petersen and Howe (1979) 7 Pommersheim (1983) 18 Pommersheim and Bell (1986) 18 Ricardo (1983) 13 Schroeder (1978) 6 Schulz (1984) 19 Shoemaker (1986) 11 Sorge and Wark (1984) 11 Stephens etai (1981) 8 Szymczuk and Frerichs (1985) 17 Webb (1985) 19 West et al (1985) 17 Widmer and Parker (1985) 20 Wileman et al (1982) 8 

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