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Visual computer simulation in instruction of apparel production Boni, Mary E. 1992

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VISUAL COMPUTER SIMULATION ININSTRUCTION OF APPAREL PRODUCTIONbyMARY ELIZABETH BONIB.Ed., The University of Saskatchewan, 1975A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF ARTSinTHE FACULTY OF GRADUATE STUDIESDepartment of Mathematics and Science EducationFaculty of EducationWe accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAAugust, 1992© Mary E. Boni, 1992In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(SignaturDepartment of 7h.tii, A" s^EThe University of British ColumbiaVancouver, CanadaDate 5- / 9 zDE-6 (2/88)iiABSTRACTThe two main purposes of this study were to explore ways in which object-basedvisually interactive computer simulation can be an effective learning environment inwhich to teach apparel production management, and to further the development ofsoftware for instruction in apparel production planning.Since students enrolled in apparel design programs typically manufacture only one ofeach design, there is no link between the design of a garment and the cost of productionon a larger scale - a critical link in industry. Setting up assembly lines in the classroomto teach production concepts would be impractical. Visits to production sites are useful,but stop short of allowing students to design and test alternative production strategies.Computer simulation provides a safe, efficient, cost-effective tool for teaching basicproduction concepts and solving problems related to production costs.Prototypes of a visual computer simulation and a spreadsheet simulation weredeveloped to teach apparel production layout design and costing. The effectiveness ofthe simulations were compared, using the nonequivalent control group quasi-experimental design approach. The researcher realized that ANCOVA was theappropriate statistical test to analyze the data as it was shown that the initial differencesin mathematical ability of the two groups was statistically significant.The study was conducted over one month. At the beginning of the experiment,instruments to identify students' thinking and learning styles and a pretest wereadministered to all subjects. Subjects in the experimental group were assigned the visualcomputer simulation exercise while subjects in the control group were assigned thecomputer spreadsheet exercise. Each group was allowed one-and-one-half hours tocomplete the assigned exercise, working in pairs. An achievement test pertaining to themathematical content of the computer exercises and drawing of a production scheme,was administered to both groups as a posttest.111Students in the group that received the visual computer simulation treatment achieveda higher adjusted mean score on a test of production costing and scheduling, although notstatistically significant, than the students who received the computerized spreadsheettreatment. The analyses indicated that there may be a directional relationship betweenstudents identified as visual learners who used the visual computer simulation andachievement on a test of production costing and scheduling as there was a significantincrease in adjusted posttest scores. The analyses also indicated that there may be a trendin students identified as active learners who used the visual computer simulation andachievement on a test of production costing and scheduling as there was an increase inadjusted posttest scores.Feedback from the students was overwhelmingly positive. Many students indicatedthat they were not strong in mathematics, but the visual simulation helped make theprocess more real to them; the calculations made sense. The enthusiasm displayed by thestudents and the surprisingly deep nature of the discussion that followed convinced theauthor that this teaching strategy was worth the effort and has considerable futurepotential.In conclusion, the visual simulation can be used in the classroom to supplementinstruction in apparel production management. Implications for future research include:testing the software with a larger sample and randomizing their distribution into groups;and probing more deeply into the nature of object-based simulation as a teaching/learningstrategy. Planned extensions for the simulation include student configurable layouts andthe typical production problems of employee absenteeism and machine breakdowns.ivTABLE OF CONTENTSPageAbstract ^ iiTable of Contents ^  ivList of Tables viiiList of Figures ^  ixAcknowledgement xChapter1 INTRODUCTION ^ 1Statement of the Problem ^ 1Rationale ^  1Hypotheses and Related Questions ^ 6Definitions of the Terms ^  7Significance of the Study 9Limitations of the Study ^  10Summary ^ 112 REVIEW OF THE LITERATURE ^  12Introduction ^  12Learning Theory 13Introduction of Computers for Learning ^  16Computer-Based Simulation ^  18The Potential of Computer-Based Simulation ^ 19Computer-Based Simulation in Education  21Producing Powerful Computer-Based Simulations ^  26Computer Simulation in Apparel Production andStudent Need for Production Management Skills ^  29VChapter^ PageSummary ^  343 METHOD AND PROCEDURE ^ 36Purpose of the Study  36Research Questions ^  36Research Question 1 36Research Question 2 ^ 37Research Question 3 38Selection of Subjects ^  38Treatments ^ 39Laboratory Setting and Procedures ^  41Research Design ^  43Nonequivalent Control Group Design ^ 43Analysis of Covariance (ANCOVA) 44Threats to Internal Validity ^ 45Threats to External Validity  46Instrumentation ^  47Thinking and Learning Styles Inventories ^ 47Pretest ^  48Posttest  49Pilot Project ^ 50Data Collection and Analysis ^ 51Overview of Data  51Summary ^ 53viChapter^ Page4 RESULTS ^  54Statistical Procedures ^ 54Disposition of Hypotheses 55Research Question 1 ^ 55Research Question 2 56Research Question 3 ^ 58Thinking and Learning Styles Inventories ^  60Thinking Styles Inventory ^ 60Learning Styles Inventory 61Summary ^  615 SUMMARY, CONCLUSIONS REFLECTIONS ON THE RESEARCHAND IMPLICATIONS FOR FUTURE RESEARCH ^ 63Summary of the Study ^  63The Nature of the Research 64Summary of Results ^  64Conclusions ^  65Limits of the Study ^ 65Learning Theories and the Experimental Treatment ^ 67Uses for the Visual Simulation in Apparel Design Programs ^69Reflections of the Research ^ 70Implications for Future Research  73Implications for Instruction^  74REFERENCES ^ 76viiAPPENDICES^ 82A. Senior Matriculation Courses Completed by Students^ 83B. Scanned Photographs of the Visual Computer Simulation Screen^ 85C. Cover Letter and Consent Form ^ 90D. Student Lesson: Visual Computer Simulation ( Experimental Treatment).. 92E. Student Lesson: Spreadsheet Computer Simulation ( Control Treatment).. 95F. Scanned Photographs of the Spreadsheet Computer Simulation Screen ^ 97G. Pretest ^  100H. Item Analysis: Pretest ^  102I. Posttest ^  103J. Item Analysis: Posttest ^  106K. Summary of Data: Experimental Group - Visual Simulation ^ 107L. Summary of Data: Control Group - Spreadsheet Simulation ^ 108vu'LIST OF TABLESTable Page3.1 Treatment Sequence ^ 423.2 Outline of the Research Design ^ 443.3 Summary Data of Pretest and Posttest 523.4 Summary Data of Thinking Styles Inventory ^ 523.5 Summary Data of Learning Styles Inventory 534.1 Visual Learners SubgroupANCOVA on Posttest Scores Controlling for Pretest Score ^ 574.2 Active Learners SubgroupingANCOVA on Posttest Scores Controlling for Pretest Score ^ 59ixLIST OF FIGURESFigure1 Visual Learners Subgrouping:PagePosttest Adjusted Mean Scores ^ 582 Active Learners Subgrouping:Posttest Adjusted Mean Scores ^ 603 Opening screen for the Visual Computer Simulation^ 864 Screen display following the selection of "Basic Skirt" ^ 875 Screen display following the connection of the workcentres^ 886 Screen display of style "w/PocicNent" in full production ^ 897 Production Times & Costs^ 94, 968 Opening screen for the Spreadsheet Computer Simulation^ 989 Screen display using "help" to answer question 1 ^ 99ACKNOWLEDGEMENTI am extremely grateful to Paul Murphy for inspiring me to take on this challenge andfor his constant support, assistance and encouragement. I sincerely thank my family,friends and colleagues for their patience and support throughout my work on this thesis,the administrators at Kwantlen College for their encouragement and support in thisendeavor and the students at Kwantlen College for their cooperation in participating inmy experiment.I wish to especially thank William Goddard for inspiring me to continue to learn howto program and for his guidance in preparing my graduate studies application.I wish to express by deepest appreciation to my advisors: Dr. Linda Peterat for hercontinuous support and guidance in assisting me toward meeting my goal; to Dr. DavidBateson for supporting my ideas and giving generously of his knowledge throughout theexperimental and analytical components of my study; and to Dr. Ricki Goldman-Segallfor giving generously of her knowledge of computers in education and herencouragement to expand my study beyond its original scope. I also thank each of myadvisors for giving of their time and assistance in the writing of this thesis. I am trulygrateful for the opportunity to have worked with each of them.Chapter 1INTRODUCTIONStatement of the ProblemThe purpose of this study is to explore ways in which object-based visually interactivecomputer simulation can be an effective learning environment in which to teach apparelproduction management.The study is motivated by the need to provide college students, enrolled in an appareldesign program, with realistic experiences relevant to the work place. The author's questis for a dynamic, visual, sophisticated, flexible, extensible, learner-friendly and cost-effective teaching/learning environment.RationaleStudents enrolling in apparel design programs enter because they are attracted by theidea of producing fashionable clothing. Most of the students have previously completedsenior matriculation home economics and art courses, but very few have completed asenior mathematics course (See Appendix A; Miller, 1991). Therefore, it appears thatthese students are more inclined toward the artistic than the mathematical. However, incommercial clothing manufacture, issues related to production management arise leadingto the all important question of cost. Cost-effective production requires well plannedbudgets, organized plant layouts, precise scheduling, efficient production methods,accurate costing and effective quality control. Knowledge of and ability to manage aproduction setup will strategically place the students in an empowering position withinthe apparel industry (Hudson, 1989). This study will focus on the management ofgarment assembly, and the subsequent costs, within an apparel design facility.Employers expect that entry-level professionals are capable of integrating andapplying their educational and training experiences to the demands of the workplace(Steinhaus, 1989). However, students have limited opportunity to experience realistic12occupational responsibilities in their college courses. In apparel design programsstudents typically manufacture only one of each design, completely missing theopportunity to study mass production techniques and their costs. Consequently, manyfine designers and technicians graduate from these programs only to fail in business or beoverlooked for promotions because of their lack of ability to supervise mass productionand optimize costs. It has been found to be difficult to set up assembly lines in theclassroom to teach these concepts, and visits to production sites are of limited value. On-the-job training opportunities are also limited.According to Alan Kay (1984), one of the leaders in computers in education, object-based visually interactive computer simulation is a new field that promises tremendousopportunities for education and training. This medium can be used to model an existingenvironment, incorporating factors that address the learning process and the learner byallowing the learner to participate in creating the learning environment. The user then,not the medium, is, to some extent, controlling the learning experience.Where realistic learning experiences relevant to the work place are required,developers of object-based models claim that this medium should provide an effectivelearning environment (Bell & O'Keefe, 1987; Page, Berson, Cheng, & Muntz, 1989).Object-based computer models can provide the one-to-one correspondence neededbetween elements manipulated by students and elements in the real world.Since the introduction of microcomputers in the late 1970s and the subsequentdevelopment of computer-based learning (CBL), computers have captivated teachers andlearners from preschool to all fields of post secondary education. In spite of warnings forcaution that the computer may not be the panacea to all that ails in education and amidstconstant controversy of the potential danger to one's health when exposed to a computerfor extended periods of time, computers have pervaded nearly every school in NorthAmerica. Microcomputers have made computing power accessible to everyone and tothe extent that they are now used in business, industry and even personal services,3educational institutions must pursue these trends if education is to play "an integral partin shaping future industrial and sociocultural developments" (Randhawa & Hunt, 1986 p.82). Sheldon (1988) found that the apparel industry is greatly increasing its use ofcomputerized equipment, and therefore, strongly advocated the use of current technologyin apparel design programs if designers are to function effectively.Increased efforts to use microcomputers to aid in facilitating the teaching and learningprocesses during the 1980s stimulated numerous studies on computer effectiveness as alearning strategy. Results are controversial; ranging from computers having positiveeffects, to being as effective as traditional modes, to displaying negative effects (Bresler& Walker, 1990). Encouragingly, student testimonials reveal an overwhelminglypositive attitude toward using computers in the classroom (Bennett, 1991; Rieber, Boyce,& Assad, 1990; Stead, 1990).The most popular forms of CBL have been "drill and practice" and "simulation".Exercises using drill and practice present users with a stimulus, elicit a response andprovide immediate feedback. Advantages to this type of instruction include allowingstudents to: work independently at their own pace; review or repeat a lesson as often asthey wish, receive immediate feedback; and start and stop when they please. Thismedium is excellent for questions requiring mathematical calculations and is also used ina variety of other contexts.Computerized instructional simulations are more complex. Briefly, summarized, acomputer-based simulation can be defined as a model that imitates some portion of ahypothetical or existing situation designed to capture the essential elements of theenvironment with the use of graphics, colour, and animation such that implications andconsequences can be determined when a course of action is applied (Bell & O'Keefe,1987; Gredler, 1986; James, 1986; P. Smith, 1986). A major advantage for students isthe opportunity to build on their knowledge through exploration and problem-solving inan environment that replicates the one in which they will work. Advantages for teachers4are that situations can be consistently replicated and presented in a variety of ways, aninfinite number of times (P. Smith, 1986).Much of the criticism of the use of CBL is related to software development failing toincorporate learning principles, drill and practice interaction restricted to enteringnumbers or words and simulations constrained by static graphics and text, or minoranimation. However, the widespread use of computers in the classroom is relativelynew, but evolving at a rapid rate, and will, therefore, require considerable time and efforton the part of software developers and educators to further exploit the promised potentialof the medium. Recent educational research studies on computer simulation recommendthat more research is needed in the development of innovative interactive computersimulations in a variety of instructional situations (e.g. Baek & Layne, 1988; McCaskey,1989; Rieber et al, 1990). Concerns and limitations to the technology are addressed bythese researchers, who also provide recommendations regarding factors that couldenhance the effectiveness of the software.An alternate approach to traditional CBL suggested by Seymour Papert (1991), apioneer in the exploration of a "constructionist" approach to educational research andpractice, claims that computers have the capacity to alter the learning process by shiftingfrom the typical instructional mode of attempting to transfer knowledge to students, tostudents producing their own knowledge. This is a powerful and challenging concept.The features of "interactive video" are also being explored in classrooms. Interactivevideo consists of a videodisc or videotape player controlled by a computer program.Educational videos can show a process with real images at a speed appropriate to theviewer, including examples of altered conditions, but the user interaction with the videovia a computer offers an exciting form of non-linear learning by allowing users to accessdifferent sections of the video.If, however, simulations are effective learning environments, why are object-basedvisually interactive computer simulations not being more widely used in education andtraining? One of the major reasons is that the programming language SMALLTALK, thewidely accepted leader in multiple object-based programming languages, has onlyrecently become available (Thomas, 1989). In many cases, the potential flexibility ofsystems like SMALLTALK can only be achieved on powerful and expensive hardware.Development of computer simulation for instructional purposes is still "leading edge", asresearchers seek to expand authoring systems into "expert systems" that will decreaseprogram design time for subject experts, and be cost-effective. With the price ofhardware decreasing and its sophistication increasing, and as object-based programmingresearch evolves, there is reason to believe that applications will become more accessibleto educators. Empirical evidence, anecdotal observations and testimonials on theeffectiveness and enjoyment of object-based applications as teaching/learningenvironments rely on case studies in areas such as the military, aviation and appliedengineering where implementation costs may not be as limiting a factor as it is in mostcolleges. Educational studies that are still in progress are emerging in the literature (SeeBorne & Girardot, 1991; Fenton & Beck, 1989; Riley, 1990; Steed, 1992), but moreempirical findings and case studies that focus on analyzing and validating theperformance of prototypical applications of object-based computer simulations ineducational settings are now needed.A survey of post secondary clothing and textiles instructors revealed that the increaseduse of computers in the apparel industry in the 1980s has led to an increased use ofcomputers in post secondary clothing and textiles programs (Knoll, 1989). However, thecomputer usage was more in the area of word processing rather than subject-specificcoursework. Results of the study showed that 49 percent of the participants selectedapparel production as the area in which the most growth in computer usage would beseen. Knoll (1989) recommended that more computer coursework must be implementedif graduating students are to function effectively in the apparel industry. The review of6the literature includes a review of current research describing the use of computers in theapparel industry and the use of computers to teach apparel production.In short, the two main purposes of this study are:• to contribute to our understanding of how object-based visually interactive computersimulations create dynamic learning environments; and• to further the development of software for instruction in apparel production planning.Hypothesis and Related QuestionsTo explore the effectiveness of an object-based visually interactive computersimulation the following hypothesis was developed:Adjusted mean posttest scores for apparel design students trainedusing a visually interactive computer simulation will be significantlydifferent on a test of production costing and scheduling than forstudents trained on a computerized spreadsheet simulation.It was expected that students using the visual computer simulation would score higheron a test of achievement related to the material presented than students using a moretraditional computerized spreadsheet. The visual simulation has the potential to providea richer, more exciting learning environment with the use of graphics, colour andanimation as opposed to a static display of rows and columns. Both treatments allow foractive rather than passive involvement for the learner by inviting the student to makeselections and elicit responses, but the visual simulation is more active as it invitesstudents to link and activate screen objects.To link learning environments to accepted learning theories, the following questionswere also examined:1. Is there a relationship between students who are identifiedas higher visual learners, the treatment administered andachievement on a test of production costing and scheduling?2. Is there a relationship between students who are identifiedas higher active learners, the treatment administered andachievement on a test of production costing and scheduling?Definitions of the TermsConcepts which are central to this study are: learning theory, learning style, visuallearners, active learners, learning environment, production management, computersimulation, visual, interactive, object-based, computer spreadsheet and effectiveness.This section will define the terms as they will be used in this study.Learning theory is an attempt by psychologists and educators to provide insight intothe processes of learning. In the context of education, events are designed to change themeaning of experience for students. The learning theories adopted by object-basedsystems developers include Papert's (1991) "constructionist theory" of building one's ownknowledge in one's own way, Dewey's (1938/1963) philosophy of "learning-by-doing"and Ausubel's "meaningful learning theory" whereby the learner must choose to relateincoming information with previously learned material (Novak & Gowin, 1986). Thereview of the literature will construct a theoretical framework linking accepted learningtheories with visually interactive computer simulation to show that the use of thismedium to teach production management training is a worthwhile endeavor.Learning styles, a component of learning theory, are the ways in which individuals areable to think and learn most effectively. Goldman-Segall (1991) takes the position that itis the learner who comes "to the subject from a variety of perspectives and thinkingstyles" and that "it is the responsibility of the educator to provide experiences within thesubject matter which open the curriculum " to the learners such that they can "make whatthey learn their own" (pp. 235, 236). Hughes invites educators to become more aware ofstudents' individual styles and how to "open educational opportunities" (cited in Guild &Garger, 1985, p. v) to everyone. For instance, it is important to people identified as78visual learners to "see" objects and activities in order to learn, while for individualsidentified as active learners, it is necessary for them to be "physically active" in someway to facilitate learning (Reinert, 1976).A learning environment is the sum total of all the factors that are brought to bear bythe conditions surrounding the student. Regarding the computer as a learningenvironment, it can provide interaction, graphics, colour, sound, text and animation.Papert (1980) and others refer to this environment as a "microworld".Production management is the controlling of the process of producing finished goods.The production division of an apparel firm plans and executes the conversion of fabricinto cost-effective finished garments within appropriate time constraints while striving tomaintain harmonious labour relations (Glock & Kunz, 1990).As previously stated, a computer simulation is a computerized model that imitatessome portion of a hypothetical or existing situation. It is designed to capture essentialelements of an environment such that changes in the environment can be affected bystudent responses (Gredler, 1986; James, 1986; P. Smith, 1986).Visual applications are dynamic, presenting information using pictures, animation andcolour as well as traditional displays of tables and charts. Visual presentations can, onone hand, allow users to more efficiently interpret large amounts of complex information("a picture is worth a thousand words"), or on the other hand, open that one image tomultiple interpretations.Interactive refers to the communication between the user and the computer. The userinteracts with the computer application used in this study by clicking on graphical objectswith the mouse. Interaction with a computer is generally limited to stimulus-responseactivities; true interaction between a computer and user has yet to be achieved since thereare many more new issues to be explored (Kay, 1990).Object-based refers to the use of computer screen "objects" in the form of iconsdesigned to look like real world objects that have individual and general properties.These computer screen objects can react to one another as a result of sending "messages"from one object to another. Users can intervene at any time to "interact" with such amodel by pressing keys or activating mouse buttons. Turkle and Papert (1991) suggestthat these objects are part of a cultural shift towards an acceptance of concrete ways ofthinking. These objects are a step toward the idea of agents acting together to produceintelligent behaviour in a society, as postulated by Minsky (1986). The objects used inthe simulation designed for this study are not agents as defined by Minsky, since theoverall effect is not an intelligent system, but merely a simulated system. Kay (1984)envisioned an agent acting as a "librarian" to assist users by "threading" its way throughextensive data bases; "a persistent 'go-fer' that for 24 hours a day looks for things itknows a user is interested in and presents them as a personal magazine" (p. 8).A computer spreadsheet is a rectangular array of columns and rows divided into cellssimilar to a paper spreadsheet used by an accountant. Each cell has a "value rule" thatspecifies how its value is to be determined. Every time a value is changed anywhere inthe spreadsheet, all values dependent on it are recomputed instantly and the new valuesare displayed.Effectiveness will be measured by the differences in pretest and posttest scores withinand between experimental and control groups.Significance of the StudyKnowledge Claim:This study will show that an object-based visually interactive computer simulationdesigned to facilitate the learning process is a more effective learning strategy than acomputerized spreadsheet for design students.910Value Claims:The simulation used in this study will provide a practical solution for instructors whoneed resources to teach apparel production management. It will provide an extensibleprototypical computer simulation that can be used in the classroom.Knowledge of and ability to manage a production setup and use a computer willstrategically place the learner in an empowering position within the apparel industry.This study may lead to further research of software applications in various domains toshow that object-based visually interactive computer simulation can promote effectiveteaching/learning where not traditionally used.This study will have implications for computer-assisted learning in general, since thefindings could assist in determining which factors are useful in enhancing students'performance in other applications of computer-assisted learning.Limitations of the StudySince intact classroom groups will be used for this study and the sample size availableis small, the generalizability of the findings are limited to the participants in the samplegroup.The programming language used to create the software for this experiment was a beta-test model, or prototype, with minimal documentation. Therefore, the programmer waslimited in the development of the model since many of the features of the programminglanguage were not as yet working to their anticipated capacity.Although a highly visual learning environment was desired, it was decided to limit thisstudy to object-based computer simulation. The incorporation of interactive video couldbe the basis of a future study.11SummaryAn object-based visually interactive computer simulation was chosen as theteaching/learning strategy for production management training, in this study, because it isuseful for demonstrating processes evolving in time. It can take several days to completea production run of a particular garment; a computer simulation can model the situationand execute it rapidly. Simulations can be designed to allow users to alter the conditionsto ask "What if..." questions and then rerun the simulation to view the results. The visualcomponent allows the user to actually see the different factory configurations orproduction parameters modelled and can choose the one that best optimizes resources(See Appendix B).A spreadsheet application was used as a comparison in this study since spreadsheetsare currently used in the apparel industry to plan production schedules, project costs andprovide updated information throughout the production process. The expected differencein the two applications is that the visual simulation will allow the user to "see" the whole(virtual) picture, whereas the spreadsheet requires the user to make hypotheticalconnections between what is displayed in the spreadsheet and what is happening on thefactory floor.It was necessary to create the simulations as existing computer simulations related togarment production planning and costing were designed for practitioners in the field ofproduction planning, not for use in the implementation of teaching/learning strategies.The software design attempted to address a variety of learning styles.It is anticipated that the students will find the visual computer simulation used in thisstudy to be intrinsically interesting. Hopefully, this research will be an inspiration forfuture probing as to why this medium is a rich format that guides learning and provides afoundation for further software development in apparel production training.12Chapter 2REVIEW OF THE LITERATUREIntroductionA common thread found to be woven throughout most of the current literature oncomputer-based learning (CBL) was an attempt by researchers to address the learningprocess; the analysis of how information is perceived, organized, reorganized, stored andapplied. Papert (1991) and his colleagues at MIT have been using the computer withchildren in schools for over two decades to study how one learns and how one thinksabout one's learning. In the context of typical classroom settings, many of the earlierstudies on CBL were generally only quantitative in nature, reporting on the effectivenessof the medium in terms of achievement in comparison to traditional modes of instructionsuch as lecture and laboratory (Bracey, 1987). Since the late 1980s most studies on CBLnot only reflected on the effectiveness in presenting instructional content provided by themedium, but addressed a sincere desire to contribute to the knowledge on learningprocesses and how to use this knowledge to improve students' learning capabilities (forexample, Bresler & Walker, 1990; Fenton & Beck, 1989; Goodyear, 1991; Kay, 1991;Riley, 1990; Steed, 1992).To explore the potential of object-based computer simulation as a teaching/learningenvironment, it was appropriate to first investigate psychological research which suggestsome of the factors that influence learning.This review of the literature begins with a discussion of learning theory and isfollowed by an evolutionary approach in the development of CBL from the introductionof the microcomputer to computer-based simulations, culminating with students creatingtheir own powerful learning environments. The final section is an analysis of theliterature related to the apparel industry to show that using computer simulation forlearning production management skills is a worthwhile endeavor.13Learning TheoryLearning is a natural phenomenon which transposes the quality of our experiences aswe move from a state of not knowing to a state of knowing. Learning something newchanges behaviour in terms of the way in which we think, feel and act about things(Gowin, 1981). Learning is a process that leads to gaining knowledge or understandingof a subject, or the acquisition of skills as a result of study, experience or teaching(Oxford, 1976). Minsky (1986) stated that "no one understands how we learn to do"(p. 21) the things that we find strange at first, but once mastered seem "mere commonsense" (p. 21). Learning theory is an attempt by psychologists and educators to provideinsight into the processes of learning to assist in developing meaningful instructionalenvironments.The literature on CBL relates to two theoretical learning models: behavioralpsychology or cognitive science. The earliest examples of CBL, usually in drill andpractice format, used a behavioral, stimulus-response, (question and answer) approach tolearning and did not refer to mental processes. With the shift in instructional softwaredesign to a cognitive approach (Tennyson, 1990), current CBL research focuses on"how" learners transform information into knowledge, and in some instances, "how"learners think about their learning, is also addressed.In a cognitive context of education, events are designed to change the meaning ofexperience for students. Two key elements in this process are: the teacher whointervenes with meaningful material, support, guidance and feedback; and the learnerwho chooses or does not choose to grasp the meaning and learn it (Hartley & Lovell,1984). If a student chooses to learn, learning becomes an active reorganization of thestudent's existing pattern of meaning; that is, the learner makes connections betweenwhat is to be learned and what is already known. Novak and Gowin (1986) consistentlyconcluded that educational experiences that did not motivate learners to grasp the14meaning of the learning task failed to give the learners confidence in their abilities anddid nothing to enhance their sense of mastery over events.Malone (1984) prescribed the interaction of three elements for an intrinsicallymotivating instructional environment; challenge, fantasy and curiosity. Students who areintrinsically motivated to learn something tend to spend more time and effort learning,feel better about what they learn, and use it more in the future (Malone, 1984).The fact that different people learn in different ways and at different rates is anextremely important concept in the planning of teaching/learning environments. Somepeople find that they can learn by reading. Others are more apt to learn something if theycan see how it operates. Some people learn best by hearing about something and others"learn by doing".Montessori (1914/1966), an early proponent of "learning by doing", used a didacticapproach to learning (instructing in a systematic, yet pleasurable manner). Montessori(1914/1966) used to advantage the natural restlessness of children by showing them withfew or no words precisely how to move their bodies to perform a particular task, forexample, tying a bow and other forms of fastening clothing. She then gave the childrenan opportunity to practice the techniques they have been shown. "Once a direction isgiven to them, the child's movements are made towards a definite end, so that he himselfgrows quiet and contented, and becomes as an active worker, a being calm and full ofjoy." (p. 21) Dewey (1938/1963), another early advocate of learning by doing, illustratedan educational strategy that combined experience, experiment, purposeful learning andfreedom, to form a philosophy within a framework of a progressive organization ofsubject-matter. In the 1960s, Carl Rogers (1969) abstracted the principle that significantlearning is acquired through doing, and that the only learning that significantly influencesbehaviour is self-discovered and self-appropriated.This concept of "learning by doing" is strongly upheld by the leading educators of thetechnological revolution (diSessa, 1986; Kay, 1984; Papert, 1980). Papert, a disciple of15Piaget, advocates that true knowledge is only acquired through experience, and thatinvolvement in "constructing" one's learning environment can be a powerful motivatingforce. Learners need to be allowed to explore, but at the same time require an externalstimulus, direction and guidance (Goodyear, 1991; Lawler, duBoulay, Hughes, &McLeod, 1986; Papert, 1980).Papert's theory of "contructionism" was influenced by Piaget's "constructivism"theory. Both theories focus on children learning by reconstructing their previouslyacquired knowledge in the building of their own models to solve problems. The twotheories differ, in that Piaget was interested in how children's mental faculties evolved atcertain stages of their life regardless of their environment (Ackerman, 1991), whereas"Papert 's research focuses on how knowledge is formed and transformed within specificcontexts" (p. 272) related to the world in which the learner lives. Also, Piaget wasmainly interested in how one constructs an internal stability in the way that one thinksabout one's world, whereas Papert is more interested in the dynamics of how one'sthinking changes (Ackerman, 1991) and does not consider age as a relevant factor in histheory. Papert (1991) claims that when children program they are teaching the computerto think, embarking on an exploration about how they themselves think. Programmingtransforms the process of learning while learning becomes more active and self-directed(Papert, 1980, p. 21).According to the "right brain - left brain" theory, both the right and left hemispheres ofthe brain are involved in equally complex higher cognitive functioning of the brain, eachside specialized for different modes of thinking (Edwards, 1979). However, most of oureducational system has been designed to cultivate the verbal, rational left hemisphere,while the other more visual, more creative half of every student's brain has been leftneglected (Edwards, 1979). In a changing world of environments with "virtual"simulations in many fields, it may be necessary to provide settings in which students canexperience shifting from one hemisphere to the next, and to encourage them to do so.16The optimal educational environment leads the learner to not only retain knowledge,but to build upon it (Kay, 1984). To meet the challenges of their future, students mustlearn to learn and to accept learning as an ongoing process, an integral part of theirlifestyle. Bruner emphasized (cited in Martin & Hearne, 1990) that learning must also betransferable to situations that are similar to the learning environment, therefore, teachersmust "select the kind of present experiences that live fruitfully and creatively insubsequent experiences" (Dewey, 1938/1963, p. 29).The questions educators face are best summarized by Novak and Gowin (1986). Howcan educators help individuals to reflect upon their experience and to construct new,more powerful meanings? How can a curriculum be built that will provide learners withthe basis for understanding why and how new knowledge and skills are related to whatthey already know, and give them the affective assurance that they have the capacity touse these new tools in new contexts?The following sections take the position that computers are being used to impact onlearning by addressing the learner and the learning process.Introduction of Computers for LearningHow can instruction be designed in a way that captivatesand intrigues learners as well as educates them?(Malone, 1984, p. 68)One response to Malone's quest is that "The microcomputers of today are theculmination of a long search for better and more efficient ways of getting things done."(Lockhard, Abrams, & Many, 1990, p. 4)The introduction of microcomputers in the late 1970s led to an exploitation ofcomputer potential in the business world with data processing, word processing andspreadsheet applications leading what appeared to be a revolutionary approach to speedand efficiency in business oriented tasks. Educators have since been searching for their17Lotus 1-2-3. What, then, has been happening in education since the early forms ofcomputer-based learning (CBL) were introduced in the late 1970s?The first drill and practice forms of CBL were criticized as being nothing more thanelectronic page turners that allowed for only minimal responses from the learner. SinceCBL was new, there was perhaps an over emphasis of concern with automation,therefore, overlooking the positive features of the medium. This form of instruction,designed to supplement rather than replace lectures and laboratories, does have a numberof advantages and has not been totally discarded even in the 1990s. Advantages include:individualized instruction and practice; repetition; feedback; and usually does not requirethe presence of a teacher. This form of individualized instruction, along with thecomputer's ability to test, grade and keep records, enables teachers to work with thosestudents who need extra attention. Also, these early programs initiated research on CBLwhich has led to the development of a wide variety of educational software in everyfield.At the same time, researchers in education were taking a cognitive science approach tothe use of computers. Papert and others studied children's learning and thinkingprocesses with children who used the computer language LOGO to program computers.The recognizable "turtle"-like object and the commands it can be given: FORWARD,BACK, LEFT and RIGHT, are all familiar body movements that children use as startingtools to explore and build their own objects with.Lawler (Lawler et al, 1986) described "playing turtle" where he and a six year oldchild moved away from the computer, and pretending to be the turtle, acted outdirections that they wanted the turtle to take. This was a chance for the child "to connecthis knowledge of himself, his own body and its movement with the new knowledge he islearning" (p. 22). The child was then able to return to his LOGO drawing of amoonscape and program the number of steps and direction of the turns that he had actedout, to get the screen turtle to form the shapes that were desired. When Lawler first18worked with the child he could not arouse the child's interest when he read to him abouthow to use the program, but as soon as they experimented with the program on thecomputer, the child became interested. This example supports the theory that somelearners need to be actively involved in their learning experience in order to learn.LOGO is used in classrooms to teach programming as well as mathematical concepts.It is conceivable that LOGO inspired the contemporary approach to programmingwhereby programming is not a goal in itself, but an opportunity for students to programmodels which become vehicles for the transmission of knowledge in a specific subjectarea (Harel, 1988).A study that summarized the major research done since 1975 on the effectiveness ofCBL in improving students' learning showed that the effectiveness of CBL has increasedsteadily since 1975 (Bennett, 1991). The three most overriding conclusions to this studywere that: students liked using the computer for instructional purposes; using thecomputer as a supplement to regular instruction increased student achievement; andstudents learned more quickly when using the computer as a supplement to instruction.Computer-Based SimulationIf an instructional aim is to assist in building models of the real world in the mind ofthe student (James, 1986), then it would appear from some of the research presented herethat computer simulation could be a solution at least some of the time. Some of theresearch on the development and use of educational microcomputer simulation, which isthe subject of this study, will be described here. This section outlines the potential of themedium, describes some of the elements found in good instructional design, cites studiesthat explored the use of computer-based simulations in education and addresses some ofthe problems and concerns related to the design and implementation of the medium.19The Potential of Computer-Based SimulationGredler (1986) summarized that a simulation used in instruction provides (1) anenvironment (a model of a realistic setting in which a problem is presented); (2)opportunities for student responses; and (3) a set of outcomes (changes in theenvironment affected by student responses). Students' previous knowledge can be builtin to the model as a stimulus to encourage attainment of higher cognitive levels(Goodyear, 1991). Simulations are usually less expensive than providing students withthe actual environment, and whenever desired, simulated situations can be consistentlyreplicated, presented in a variety of ways, an infinite number of times (P. Smith, 1986).Simulations can incorporate Malone's (1984) elements of an intrinsically motivatinginstructional environment: challenge is met by allowing for a variety of outcomes thatcan be learner controlled; by deviating from reality, fantasy is incorporated; and curiositycan be aroused by progressively increasing the complexity of the tasks as the studentsuccessfully completes each task. A powerful learning situation is created byenvironments that allow students to attain goals through discovery of new skills andknowledge (Papert, 1980). Simulations provide this opportunity for learners to exploreand problem-solve by asking "What if . . . " questions (Kay, 1984). Prompt feedback tothe learners on their actions frees them to take risks and experiment with decisions. Byallowing for student manipulation of the environment, simulations generally lead toinvolvement with the subject matter.The capabilities of computer-based simulations go beyond providing these problemsolving and exploratory teaching/learning environments. Computer-based simulationsare used in contexts where performing the necessary activities might otherwise bemorally implicating, time-consuming, very complex, dangerous or expensive (James,1986). For example, the cost of designing bridges, experiencing engine failure,performing tests on animals, the number of variables in the design of a manufacturing20setting or modelling geological processes are often impractical to explore in real life(James, 1986).Computer-based simulations can optimize the function of the computer. Themodelling of a realistic situation is dynamically presented in a two-dimensional window.It can be pictorial and animated, modelling processes instead of static concepts.Computer-based simulations are programmed to allow the models to operate according toa combination of rules and random processes (Walker, 1983). Student involvement isattained by controlling the computer simulated world using the keyboard or a mouse.Effects of responses can be viewed immediately. This type of environment enablesstudents to learn abstract relationships more easily than by reading or being told them(Walker, 1983). Metaphors are being used in computer simulations to guide studentsfrom the familiar to the unfamiliar. It is this metaphorical approach combined with theinteractiveness of the medium that provide students with the leverage needed to react to amultiple of possible outcomes (Kay, 1984). The kind and amount of knowledge gainedneed not be predetermined by an outside agent; it can be constructed by the student.Students are, therefore controlling their own learning experience.Even with the qualities accredited to computer-based simulations, educational softwarehas its problems and limitations. Much of the existing software lacks sophistication todeal with complex situations. In many instances teachers who use computers in theclassroom have become frustrated with them, as existing programs do not adequatelymeet their needs. Generally, educators have neither the time nor the inclination tobecome programmers, so they have had to rely on computer programmers who are notnecessarily educators. Even when programmers do consult educators, the process ofcreating software is still time consuming and therefore costly. Additional barriers inusing computers in education include the cost of hardware, training, equity, leadership,support, cultural bias, inconclusive evidence that learning has been improved, fear of the21unknown and social/psychological issues that have been raised in relation to extended useof computers.Computer-Based Simulation in EducationIn spite of the concerns associated with computers there is an abundance of literatureon computer-based simulation in education. The simplest forms of computer-basedsimulations were designed using text, the next level of sophistication incorporatedspreadsheet applications and most contemporary simulations include colour, graphics oranimation.A text-based computer application, that simulated an MS-DOS environment to teachthe use of DOS commands, was used as an enhancement to previously existing coursematerials in a management information systems course and to provide an opportunity forstudents to become active participants in their learning. Results of the study showed thatstudents with a higher level of computer usage performed better on course assignmentsand quizzes than students with a lower level of usage. It was concluded, therefore, thatactive involvement in one's learning appears to be an enhancement to the learningacquired (Atkinson & Burton, 1991). This conclusion is consistent with Bennett's (1991)findings.Computer spreadsheets imitate an accountant's paper ledger of rows and columns.Cells represent each figure in the rows and columns, and as one cell is altered the othersrespond according to the specific direction they have been given. As well as recordingthe past, this medium can be used to forecast the future by inputting data and asking"What if . . ." questions. It would appear that computer spreadsheets are a powerful formof simulation.Stead (1990) used a computer spreadsheet simulation application, "Running the BritishEconomy", to provide an opportunity for students to experience problem-solving and tostudy the capabilities of simulations as learning environments. If one accepts that22learning is the process of redefining previous knowledge, then Stead (1990)recommended that students be provided with sufficient background knowledge prior tointroducing them to the simulation. Two limitations to the medium that this studyunveiled were: (1) that models might present discrepancies due to change over time (e.g.change in interest rates); and (2) the potential for information-overload. The simulationdid, however, capture the students' interest and they found it to be a pleasurable learningexperience, "not normally a salient feature of economic courses" (Stead, 1990, p. 115).A spreadsheet software package, designed for chemical engineering students, was usedto simulate chemical processes in a chemical plant (Gilabert & Gavalda, 1990). Thestudents had considerable success in solving problems by analyzing the computergenerated calculations, and it was found that the students who were exposed to thesimulation for six hours rather than three were the most positive about the simulation(Gilabert & Gavalda, 1990).Humans and computers communicate through a contact surface referred to as an"interface" which is most often equated with the software displayed on a computer screen(Laurel, 1990). Components of interface design include pointing devices, windows,menus, colour, graphics and animation. As software sophistication has increased so hasthe need to address the "user-interface" so that the potential of the software can beexploited. Colour, graphics and animation should illustrate the important features of thematerial being presented (Baek & Layne, 1988). The following studies show ways inwhich colour, graphics and animation have been used to enhance the learning process ininstructional software design.It is essential that the use of colour be appropriate to its purpose since colour is animportant communication aid in computer output (Thorell and Smith, 1990) . Colourusage in computers, rather than monochrome, is preferred because colour images moreclosely represent the appearance of real images and when used appropriately, enhance thelocation, grouping, coding and memory of images (Thorell & Smith, 1990). Colour is a23primary factor in drawing attention to a computer program, can affect the users emotionsand it has been shown that the use of colour can be used to enhance learning.Thorell and Smith (1990) identified a number of uses of colour in educationalcomputer applications. Colour coding is used on maps when trying to identify specificfeatures. Spreadsheets and graphs use colour to highlight and group complexinformation. Colour aids in the visualization of the iterations of shapes derived fromrecursive mathematical equations called fractals. In simulations used to show productiontracking in a manufacturing setting, moving targets are better identified if they are incolour. In complex manufacturing displays, colour can be used to identify the variouselements of the system.Computer graphics are visual outputs in the form of graphs, charts and pictorialrepresentations, as opposed to alphanumeric information. Graphs reveal data by showinga range of values against a scale. It is often easier to interpret data from a properlydesigned visual representation of the numbers than from a list of numbers. Computergraphs can be produced more quickly on a computer than by hand, and once created areeasy to alter and reuse.For a construction project, engineering students used a spreadsheet model whichautomatically computed and generated a resources requirement schedule in graphicalform when the predetermined activity start times were input. Students claimed that theycould better understand the concepts behind job scheduling techniques when they werefreed from the tedious task of manually computing the volume of computations requiredin a construction project. They spent their time doing higher level learning by analyzingthe results shown on the graph (Premachandra, 1991). Peck and Pargas (1991) supportedthe use of graphing data for analysis in operational settings due to the volume of detailthat needs to be assessed.Graphing can be applied to a variety of subject areas; consumer purchasing patterns24can be graphed and used to forecast future market trends, and geological processes can begraphed to show change over time,Computer graphics are also pictorial representations, either as icons that are simplesymbolic representations, or as objects with sufficient detail to be recognized as realobjects. Spencer (1991) focused on "pictorial representation" in teaching materials. Hefound that "decorational" graphics had no effects while "representational" graphics usedin educational media and methods could aid recall, comprehension and understanding.He also found that the most effective methods of instruction included individualizedlearning that provided diagnostic and remedial feedback combined with media thataddressed both the verbal and image systems of the brain (Spencer, 1991). This supportsthe previously mentioned "right brain - left brain" theory of learning. Spencer (1991)also found that the computer is most successful when tutoring or interactively simulatingreal world events, and that simple line illustrations are as effective as more complex,realistic representations. This supports James' (1986) claim that an attribute ofsimulation is the opportunity to remove unnecessary complexities found in reality toallow the student to concentrate on the fundamental process under study.Sachter (1991) found that students gained a mastery of spatial concepts bycoordinating both rotation and perspective using a 3 - D computer graphics program.Since the students needed certain mathematical knowledge in order to create and rotateimages on the computer they were also learning mathematics as they actively applied themathematical concepts needed to develop the desired spatial relations between the objectson the computer screen.In computer-based simulations graphics can be static objects in which the imageremains constant, or dynamic objects which can be seen in operation with the use ofanimating techniques. A simulation to study the relationship of the visual effect ofanimation in a lesson on Newton's Law of Motion, gave students control over ananimated starship by allowing them to manipulate the direction and frequency of forces25acting on the starship (Rieber et al, 1990). Students who used the animated graphicsfound them to be helpful in their learning and "fun practice". In comparison, studentsusing static graphics or no graphics said that the lesson should have included pictures andgraphics with examples of movement to enhance their ability to grasp the concepts andaid in the retrieval and reconstruction process on a posttest.Peck and Pargas (1991) stated that techniques such as animation are inappropriatewhen simulating a large, complex environment, such as a an entire factory, because theviewer cannot comprehend how an operation is progressing while watching an animationthat incorporates a lot of detail.Problems inherent in the software used in most of the examples cited include limitedopportunity for student or teacher input to the systems in order to vary a model'sparameters and limited opportunity for students to really experience results of theiractions. Riley (1990) found that students gained a general impression of a hydrologicalsystem when they used a dynamic computer simulation which provided an opportunityfor them to vary the model's parameters to study the effects of rainfall. However, thestudents had difficulty in explaining the behaviour of the system. The study alsoidentified some of the problems with the design of the software and suggested to theresearcher that students might learn more or understand better if they researched anddeveloped their own computer models (Riley, 1990).Some of the contemporary literature focuses on providing students with an opportunityto create their own models in place of existing controlled models (Borne & Girardot,1991; Fenton & Beck, 1989; Goodyear, 1991; Harel & Papert, 1991; Riley, 1990; Steed,1992). Student model making is especially prevalent in subjects requiring anunderstanding of the functions and changes over time found in "dynamic systems".Riley's (1990) and other educators' realization that important learning comes throughexperience and discovery takes us back to Papert's (1980) constructionist theory. Amajor deterrent in the use of student model making to date is the current lack of anauthoring system that is flexible and easy to master in a short period of time. The nextsection will look at the research on developing the tools needed to produce powerfulcomputer-based simulations.Producing Powerful Computer-based Simulations"The power of computer simulation is that it allows interaction to take place. Atanytime, the simulation may be halted and changes made to the parameters, or reportsviewed to assess how the simulation is proceeding" (Harlock, 1989, p. 22). In amanufacturing setting, for example, if a bottleneck is identified, then workcentres ormachine operators can be moved, and the effects of the changes can be determined on thenext run of the simulation. This activity allows for experimentation with the opportunityfor users to see, almost immediately, the consequences of their actions. How can thistype of simulation be produced?Object-oriented programming languages are powerful enough to develop models thatcan provide a one-to-one correspondence between a real world object and acomputational object (Kay, 1984). Object-oriented refers to the use of computer screen"objects" in the form of icons designed to look like real world objects that haveindividual and general properties. These computer screen objects can react to oneanother as a result of sending "messages" from one object to another. Users canintervene at any time to "interact" with such a model by pressing keys or activatingmouse buttons.LOGO, developed by Seymour Papert (1980), is the simplest and most widely knownof the object-oriented environments. The language is built on a "turtle" metaphor. Thereadiness with which a young child can comprehend the "turtle-ness" of a small trianglethat can be made to move and draw lines is testimony to the power of an appropriatemetaphor. LOGO is, however, limited in that it uses only one object. A desire to expandLOGO capabilities to a broader range of activities motivated work on BOXER (diSessa2627& Abelson, 1986). Other environments, most of which are still in their developmentalstages, have the capacity to bring a multiple of objects into play.SMALLTALK is the leader in multiple object-based programming languages. It is apowerful tool for developing interfaces and interactive environments (Borne & Girardot,1991). However, it is a complex language and, therefore, takes considerable time tolearn. This type of authoring language usually requires a programmer to producecourseware. An authoring system is a layer on top of the underlying language that isdesigned to be more accessible to nonprogrammer subject experts. Some of theauthoring systems act like templates and others provide a variety of predesigned toolsthat can be manipulated by the user.REHEARSAL WORLD was implemented in SMALLTALK (Finzer & Gould, 1984).The programmable components (analogous to the cells in of a spreadsheet) use theatricaluser-interface metaphors. The user sends messages to performers, telling them to dospecific tasks (display a message, calculate a number, retrieve data). The emphasis inthis environment is that it is graphical, allowing for visual programming so that non-programmers can create software easily and quickly.Alternative Reality Kit (ARK), an animated programming environment implementedin SMALLTALK, was based on a "physical objects" metaphor (Smith, 1986). Theobjects have velocity and mass. For example, the laws of gravity are presented in theform of concrete objects. The primary motivation for this project was to simplifyperplexing abstractions. This provides an alternate strategy for teaching principles thatare not easily grasped.Alan Kay's Vivarium Project (cited in Rose, 1987) developed and explored a computerprogram that allows children to create their own exploratory plant and animalenvironments. One purpose of this project was to assist children in becoming a part oftheir learning experience by building and investigating their own microworlds. UsingPLAYGROUND, an object-oriented programming environment implemented in28SMALLTALK N and C, children constructed simulations by creating objects in theshape of animals, gave each of their artificial animals "laws" to obey, let them loose inan artificial environment and then observed their behaviours (Fenton & Beck, 1989).These objects are a step toward the idea of agents acting together to produce intelligentbehaviour in a society, as postulated by Minsky (1986). To make programming easier,PLAYGROUND uses a syntax that closely resembles the syntax of a natural language.Goldman-Segall (1991a) created and used a "unique multimedia research environmentcalled Learning Constellations" (p. 467) which combines videodiscs and a specificallydesigned HyperCard (computer) application, developed for researchers to build theoriesfrom video-based data. HyperCard-based applications are developed in HyperTalk, acomputer language that is relatively easy to learn and use. HyperCard simulations arecurrently being developed and verified in educational settings (Guimaraes & Dias, 1992).The growth of microworlds is now evident in commercial systems that reflect thevision and research of the early 1980s. There are many object-oriented productsavailable today. "The suitability of a language for modelling is a measure of its ability tosupport the creation and execution of objects that represent the objects being modelled."(Morton, in press) Some examples of object-oriented products that have been used todevelop manufacturing simulations are mentioned here.CINEMA, an authoring system written in the SIMAN simulation language, usesgraphics to produce animated systems with problem solving potential (Systems ModelingCorp., 1988). Legault (1992) used a simulation model developed withSIMAN/CINEMA to experiment with a modular production system to assist a dressmanufacturing company in its decision to invest in equipment and training in order toimplement the production system. Steed (1992) described three types of simulationconstruction kits that are designed to assist users in developing their own simulations andfacilitate dynamic systems thinking; DYNAMO, STELLA and EXTEND.SIMFACTORY, implemented in PC-SIMSCRIPT was designed to provide a standard29tool for realistic factory analysis without programming (CACI, 1987). Harlock (1989)developed one of the first production simulations specifically for clothing manufactureusing the SEE WHY simulation tools program.AUDITION (Synaptec Holdings Ltd., 1990), the programming language used for thisstudy, is a PC-based object-oriented computer modelling environment that extendsREHEARSAL WORLD's theatrical paradigm. The objects in a simulation are"Performers" with independent intelligence that co-ordinate their activities by sendingand receiving messages, called "Cues", on platforms called "Stages". Through its visualinterface, AUDITION provides a menu of options from which the types of Performersused in the simulations are created and then customized. AUDITION follows the 'noun-verb' style of object-oriented programming. The basic idea is that the programmer tellsAn Object to do This Action using These Arguments. Processes can be explicitlyscheduled using a Scheduler which schedules events at a particular point in time. Time ismanaged by an Event Monitor. Statistical support includes both the ability to generaterandom numbers from various distributions for input to a simulation and support inanalyzing data that is generated by a simulation. Reporting is provided by Graphs,Gauges and Spreadsheets.Although the programming tools described here are intended to be easy-to-use, manyare still at the prototypical stage and, therefore, are not necessarily easy to master. Also,creating an effective simulation requires rigorous thinking in the design of the modeland to make the model function within the software system (Steed, 1992). Research inobject-oriented programming environments is ongoing.Computer Simulation in Apparel Production and Student Need for ProductionManagement SkillsTo compete with the increasing influx of low cost imported apparel, North Americanapparel manufacturers need to reduce costs (Shelton & Dickerson, 1989; Warfield, Barry,30& Anderson, 1986) by improving production efficiency (Forney, Rosen, &Orzenchowski, 1990; Sheldon, 1988). Also, consumers have become more sophisticatedin their preferences for high quality and variety in fashionable apparel which requiresenormous flexibility in production (Friese, 1986; Hallem, 1990). Solutions are beingfound in the use of computers in all areas of the apparel manufacturing business fromsales orders processing to the scheduling of production lots and tracking of work-in-progress.Retailers face a number of problems in dealing with imports, such as lengthy leadtime, inability to control quality (Warfield et al., 1986) and limited opportunity to reorderitems that are selling well. As North American manufacturers'have the advantage ofclose proximity to domestic markets, they are combating imports with the use of "QuickResponse" (QR). QR is a computerized system that links retailers with manufacturersand manufacturers with suppliers.QR has become a way of thinking that is revolutionizing the garment industry (Staff,1987). It now incorporates computer-aided design (CAD), computer-aidedmanufacturing (CAM) and computer-integrated manufacturing (CIM). The featurearticles in top trade publications for the apparel industry (for example, Bobbin,Readywear, & Apparel Industry) regularly focus on apparel industry innovations,suggesting that, "the computer is the ultimate weapon to address the changing demandson apparel manufacturers" (Turner, 1990). Most companies using computerizedproduction systems have reduced fabric waste, increased productivity and improvedgarment quality which has increased their ability to compete (Forney et al., 1990; Walsh,1989).Along with buying new equipment to compete in the current market place, apparelmanufacturers have also had to consider changing their way of doing things. Althoughthe traditional "bundling system", where each machine operator is responsible for onestep in the construction of the garment as bundles of garment pieces move from one31machine operator to the next, is still being used in production, many apparel companiesare now using a "modular system" to produce smaller lots, especially repeats, quicklyenough to respond to the demands of the retail stores (Legault, 1992). Modularmanufacturing refers to the conceptual approach of having machine operators work as ateam, whereby, each operator is required to do a number of different tasks as needed andgarment pieces rather than bundles move through the system. Research results show thatusing the modular system has increased production planning flexibility, labourefficiency, throughput times, net productivity and morale as well as an improvement inquality (Hill, 1992).Interestingly, the first seeds of data processing machines were planted in the textileindustry by Jacquard, in 1790, who constructed an automated loom controlled by a seriesof punched cards to weave fabric. This technology is still used today in modern textileplants.Computer systems specific to the apparel industry were introduced in the late 1960s(Tray, 1986). However, computer simulation, although used in manufacturingthroughout the 1980s, is new to the clothing industry (Harlock, 1989), especially in avisual form. Most existing interactive simulations are in the form of a computerizedspreadsheet. A computer simulation is a relatively inexpensive risk-free opportunity thatcan shorten response time by solving manufacturing problems at the production planningstage.The first minicomputer system for pattern grading and marker making was installed inVancouver in 1982 with five more installations in 1985. Now that more reasonablypriced systems that run on microcomputers are available, the number of companies usingcomputers for manufacturing components of their businesses has increased to abouttwenty.Rapid changes in the apparel industry directly impact upon the educational andtraining requirements of apparel design students. Apparel manufacturers need entry-level32designers who can communicate with production personnel and be able to adapt toconstant changes in job performance demands (Scheres-Koch, 1988; Sheldon, 1988)."An employee who can accurately cost a garment is a tremendous asset ... Efficientcompanies cost a garment soon after the sample is completed for assistance in weighingits merit as a potential addition to the line." (Hudson, 1989, pg. 131) These skills arerequired in all aspects of apparel manufacturing from the design and merchandisingthrough to the production management department. Opportunities should exist within thecurriculum that build on students' existing knowledge to assist them in the constructionof new learning experiences so that they can gain confidence before entering the workforce.Many apparel design programs across the continent are responding to the changingemployee qualification requirements of apparel manufacturers by implementingcomputer related learning activities. Instructors of design programs, who are usingcomputers to teach, are pioneers in the development of computer-assisted learningmodules for apparel design; few appropriate commercial products exist. Most of theresearch on the use of computers in apparel design programs can be found in the annualmeeting proceedings of the International Textile and Apparel Association (ITAA;previously called the Association of College Professors of Textiles and Clothing:ACPTC). Knoll (1989) summarized the research on computer use within the academicbody of post secondary clothing and textiles from 1980 to 1987. By 1985 Miller andDejonge's students were using Auto CAD to create garment design variations, inputpatterns, grade patterns and create markers (cited in Knoll, 1989, p. 41). The number ofresearch presentations on computer use in post secondary apparel design programs hasdramatically increased since 1987. ITAA annual meetings now include a "special topicssession" on computers. Also, a special conference (Holloway & Ledwith, 1989) a specialjournal (Rabolt, 1990) and a special resource display at the 1991 ITAA annual meetingwere dedicated to computer use in apparel design programs. Computers are now used to33teach a wide variety of topics in apparel design, including: garment design; textiledesign; pattern making; pattern alterations; pattern grading; marker making;merchandising math; and retail store layout planning.Four studies directly related to this study were identified in the ACPTC and ITAAgeneral meeting proceedings. Ford, Kunz and Glock (cited in Knoll, 1989, p. 49) andMiller (1991) customized the spreadsheet software program, Lotus 1-2-3, to teachapparel costing and concepts of the production process. At the Apparel ComputerIntegrated Manufacturing centre, established at the University of Southwestern Louisianain 1988, students use state-of-the-art industrial CAD/CAM systems to study productionplanning (Im, 1991). The fourth study described the role of the Textile ClothingTechnology Corporation (TC2); a non-profit resource centre for the American apparelindustry and educators that provides training in costing and production management(Fraser, Christman, Else, Hughes, & Glock, 1989). TC 2 has developed a number ofvisual interactive computer-based simulations on several different apparel productionsystems using SIMAN and CINEMA software development tools. However, thesesimulations were designed for practitioners in the field of production management, notfor use as teaching/learning strategies.O'Riley (1988) stated that textile and clothing instructors are continuously looking fornew and better ways of enhancing students' visual thinking and communication skills.Her research focused on using a visual computer program in apparel design to encouragestudents to think and communicate visually.To stay abreast of changes in the industry, in 1989, the Fashion Design andTechnology Program at Kwantlen College added computer-aided pattern grading andmarker making to the curriculum using an industrial CAD system. Garment costing is anintegral component of the program whereby students use a spreadsheet application thatincludes general categories such as materials, production, and overhead to establish thewholesale and retail costs of a specific garment. However, a guide to industrial34production costs is supplied to the student. The student does not actually divide thedesign into its individual components to determine the production cost, which in amanufacturing setting is based on the number of pieces to be sewn, the amount of timefor each operation and the wages for the machine operators. To compensate for thisdeficiency, an option course that includes Time-and-Motion studies that providesstudents with an opportunity to learn how costing of individual operations is arrived at,was recently added to the curriculum. It was anticipated that students will have a betterunderstanding of production costing upon completion of this course. However, they willnot have had the opportunity to "experience" the production flow of mass producing onestyle followed by another, as it is done in an industrial setting.Several design and clothing and textiles programs across Canada have incorporatedcomputers into their curriculum. For example, to teach garment design, pattern making,grading or marker making, colleges such as Ryerson Polytechnical Institute and LaSalleCollege use industrial CAD systems and the Universities of Manitoba and Alberta areusing programs developed with Auto CAD.SummaryFrom the discussion on learning and learning theory, it would appear that the optimaleducational environment leads learners to want to learn by providing opportunity for:discovering and building on their personal and scholastic experiences; becoming familiarwith the ideas of others; being intrinsically motivated to grasp meanings; learning bydoing; being involved in choosing their learning experience; shifting from onehemisphere of the brain to the other; relating the subject matter to their own purposes;transferring knowledge to similar environments; and learning how to learn as an ongoingprocess.Studies cited, reflected these elements of the learning process in a variety ofcomputer-based learning environments, thus, suggesting that the medium provides a35positive learning environment. In addition to addressing the learning process, the use ofcolour, graphics and animation has expanded the potential to produce dynamic computer-based simulations that are intrinsically captivating. Object-based authoring systems arethe current technology used to produce powerful computer-based simulations.There have been many technological changes in the activities of the apparel industry.There is reason to believe that changes in all industries will continue at a fast pace. It istherefore necessary that students become skilled learners. To provide students with anopportunity to "experience" the production flow of mass producing one style followed byanother, as it is done in an industrial setting, a visually interactive computer simulationon production management will be implemented and its effects as a teaching/learningstrategy will be compared with a computerized spreadsheet simulation. The simulationaddresses issues cited in the review of the literature; students will have the opportunity tobuild on their knowledge through interactive exploration and problem solving in a visualenvironment that replicates the ones in which they will work.The analyses of this study will argue that a dynamic, sophisticated computer-basedsimulation can provide an effective teaching/learning environment, to teach mathematicalconcepts to students who tend to be more inclined toward the artistic than themathematical.Based on the review of the literature, it is anticipated that this study will provide apractical solution for instructors who need resources to teach apparel productionmanagement and contribute to the implementation of computer-assisted learningenvironments in apparel design programs. It is also hoped that this research willcontribute to the research and development of visual computer-based simulation andprovide further insight into factors that enhance student learning.Chapter 3METHOD AND PROCEDUREThe purpose, research questions, selection of the subjects, treatments, laboratorysetting and procedures, design of the study, instrumentation, pilot project and datacollection and analysis are described in this chapter.Purpose of the StudyThe purpose of this study is to explore ways in which object-based visually interactivecomputer simulation is an effective learning environment in which to teach apparelproduction management.An object-based visually interactive computer simulation was chosen as theteaching/learning strategy for production management training, in this study, because it isuseful for demonstrating processes evolving over time. It can take several days tocomplete a production run of a particular garment; a computer simulation can model thesituation and execute it rapidly.A spreadsheet application was used as a comparison in this study since spreadsheetsare currently used in the apparel industry to plan production schedules, project costs andprovide updated information throughout the production process.Research QuestionsThe data gathered were used in a statistical analysis to test the following hypotheses:Research Question 1 Ho :^The adjusted mean posttest scores for apparel design studentstrained using an object-based visually interactive computersimulation will not be significantly different on a test of36production costing and scheduling than for students trainedon a computerized spreadsheet simulation.== experimental (visual simulation) groupIlx2' = control (spreadsheet simulation) groupH 1 :^The adjusted mean posttest scores for apparel designstudents trained using an object-based visually interactivecomputer simulation will be significantly higher on a testof production costing and scheduling than for studentstrained on a computerized spreadsheet simulation.Two further questions, with the alternative hypotheses being non-directional since thedirection of the results were unpredictable, were also tested.Research Question 2Ho :^The adjusted mean posttest scores for apparel designstudents identified as higher visual learners willnot be significantly different on a test of productioncosting and scheduling from the lower visual learner group.tiVli t = VI:ilV11 1 = higher visual learners group= lower visual learners groupH 1 :^The adjusted mean posttest scores for apparel designstudents identified as higher visual learners will37be significantly different on a test of productioncosting and scheduling from the lower visual learners group.VI:Research Ojjestion 3 Ho :^The adjusted mean posttest scores for apparel designstudents identified as higher active learners willnot be significantly different on a test of productioncosting and scheduling from the lower active learners group.PAH t^ALIPLAH ' = higher active learners groupgm: = lower active learners groupH l :^^The adjusted mean posttest scores for apparel designstudents identified as higher active learners willbe significantly different on a test of productioncosting from the lower active learners group.11A11 1 11 AI:Since research question one has a directional H1 a one tailed test can be used.However, for questions two and three, a two tailed test must be used.Selection of SubjectsThis study was undertaken at Kwantlen College in Richmond, British Columbia,Canada. Kwantlen College is a community college, located in the south Fraser region ofthe lower mainland, that offers a range of courses and programs. The researcher is an3839instructor in Kwantlen's two-year Fashion Design and Technology Program. Participantsin the study were students enrolled in one of the courses in this program.The participants were recruited from fifty-five first-year college students enrolled intwo sections of a Development of the Apparel Industry course taught by the researcher.Students received a covering letter and consent form to sign (See Appendix C) . Thestudy was in the context of the material normally covered in the course with the posttestmaking up ten percent of the final mark for the course. Marks were not affected if astudent chose not to participate in the study. All fifty-five students agreed to participate,however, three students were eliminated from the study because they missed one or moreof the components of the study.Intact classes were used for the study rather than randomly assigning the students totwo groups so that the normal course of events was maintained. A coin was flipped todetermine which group of students would receive which treatment. All of theparticipants were offered the opportunity to use the simulation that they did not use in thestudy after the experiment was completed.TreatmentsThere were two treatments given; a visual computer simulation (See Appendix D) anda spreadsheet computer simulation (See Appendix E). The two computer simulationsused for this study were designed and developed by the researcher using a beta-testversion of the AUDITION computer language (Synaptec Holdings Ltd., 1990).It was necessary to create the simulations since existing computer simulations relatedto garment production planning and costing were designed for practitioners not for use inthe implementation of teaching/learning strategies.AUDITION is a PC-based object-oriented computer modelling environment based ona theatrical paradigm. The objects in a simulation are "Performers" with independent40intelligence that co-ordinate their activities by sending and receiving messages, called"Cues", on platforms called "Stages".Through its visual interface, AUDITION provides a menu of options from which thetypes of Performers used in the simulations are created and then customized usingAUDITION's Model Editor. AUDITION follows the 'noun-verb' style of object-orientedprogramming. The basic idea is that the programmer tells An Object to do This Actionusing These Arguments. The cue representing the action to be performed is sent to theobject. The object concerned is known as the receiver and the action as the cue. Thereceiver is, therefore, the 'noun' and the cue the 'verb'.Along with the customizing of Performers and the invoking of cues from thePerformers' Behaviour Editor, simulation modelling in AUDITION requires two morefacilities: scheduling and statistical support. Processes can be explicitly scheduled usinga Scheduler which schedules events at a particular point in time. Time is managed by anEvent Monitor called MainEvents. A cue to MainEvents resets its clock at zero andstarts it running. Another cue stops a simulation clock once MainEvent's clock reaches agiven time. Statistical support includes both the ability to generate random numbersfrom various distributions for input to a simulation and support in analyzing data that isgenerated by a simulation. Reporting is provided by Graphs, Gauges and Spreadsheets.The visual computer simulation (experimental treatment) developed is a prototype toteach apparel production layout design and costing to fashion design students. Colourgraphics display a simulated factory layout that includes gauges, graphs and a costingsheet to show the flow of goods and the associated costs. Garment designs are dividedinto their individual components to determine the production cost which, in amanufacturing setting using the progressive bundling system, is based on the number ofpieces to be sewn, the amount of time for each operation and the wages for the machineoperators.41Students use the simulation by selecting options from menus with a mouse. There arethree options which are progressively more complex in design: (1) a basic skirt; (2) askirt with side inseam pockets; and (3) a skirt with side inseam pockets and a back vent(See Appendix B). Selecting a scenario displays a layout of a factory floor with sewingmachines representing the steps in the construction of the garment. Selection of optiontwo will display one more sewing machine than option one and selection of option threewill display two more machines than option one to show that more complex designsrequire more machinery and more operators which will, therefore, increase the total costof the garments. A spreadsheet in one corner of the screen displays the average times forcompleting each step. Each sewing machine has two gauges which rise and fall with theflow of parts in and out of the workstation. A graph in another corner of the screenmonitors aggregate production. Each run is different because the underlying model is asimulation driven by probabilities (as opposed to a more deterministic simplespreadsheet). Students can stop the simulation at any time and restart. A "help" optionprovides assistance with the use of the program and the mathematical calculationsrequired.The spreadsheet computer simulation (control treatment) developed is the same as thespreadsheet component of the visual simulation (See Appendix F).Laboratory Setting and ProceduresThe covering letter accompanied by the consent form was distributed and the consentform was collected during a regular class session one month prior to the experiment. Theresearcher read the covering letter aloud and gave the students an opportunity to askquestions related to the procedure.The thinking and learning styles inventories and the pretest were administered at theend of three different regular class sessions in the regular classroom setting, two weeksprior to the experiment.42Microprocessors (386 DOS based), colour VGA monitors and serial mice wererequired for the software used in this study. After considerable research into thepotential computer facilities available to the researcher, eight appropriate hardwareconfigurations were located in the Mass Communications and Journalism Programmicrocomputer laboratory at Kwantlen College. Advance assistance from the laboratorytechnician was needed to load and test the software. Timing for the experiment wasbased on the regular class time for the course, to allow for minimum disruption, and theavailability of the computer laboratory.Students were introduced to the topic on Mass Apparel Production and given ademonstration of the computer program in a two hour class preceding the experimentaldate. The experiment took place on Tuesday, November 19, 1991. Table 3.1summarizes the treatment sequence. Each of the treatment groups were divided into twogroups, and due to the time constraints and limited number of computers available,students worked in pairs at the computers.Regular class time for the participants in the experimental group was Tuesdays andThursdays from 8:00 a.m. to 10:00 a.m. Regular class time for the participants in thecontrol group was Tuesdays and Thursdays from 1:00 p.m. to 3:00 p.m.Table 3.1Treatment SequenceIntact8:00- 9:10a.m, 9:30-11:00a.m. 12:00-1:30p.m 1:30-3:30p.m.ControlExperimental Experimental ControlClasses Group Group Group GroupNo. ofstudents 14 13 14 1343In the experimental groups, students took from one hour to one-and-one-half-hours tocomplete the exercise. In the control groups, students took from fifty minutes to one-and-one-half-hours to complete the exercise. Every student was able to finish theexercise assigned in the allotted time.The posttest was administered one week after the experiment during a regular classsession.Research DesignThe experimental design and procedures are summarized in this section. As well,potential threats to internal and external validity are discussed.Nonequivalent Control Group DesignAn experimental design was selected, for this study, as an initial approach in theexamination of the use of a visually interactive computer simulation as a practicalsolution for instructors who need resources to teach production management. Sinceintact classes were randomly assigned to treatments, the nonequivalent control groupquasi-experimental design approach as outlined by Campbell and Stanley (1963, pp. 47-50) was used. Random assignment of individuals to treatment groups was not possibleand the sample size was small, therefore, analysis of covariance (ANCOVA) wasdetermined to be the appropriate test to study the effect of the treatments (Campbell &Stanley, 1963). To measure the treatment effects, variations in class means fromposttest results were analyzed by ANCOVA using a pretest means as the covariate.At the beginning of the experiment, instruments to identify students' thinking andlearning styles and a pretest were administered to all subjects.Subjects in the experimental group were assigned the visual computer simulationexercise while subjects in the control group were assigned the computer spreadsheetexercise. Each group was allowed one-and-one-half hours to complete the assignedexercise.44An achievement test pertaining to the mathematical content of the computer exercisesand drawing of a production scheme, was administered to both groups as a posttest .Table 3.2 shows a diagram of the research design.Table 3.2Outline of the Research DesignExperimental Group^0 1^X1^02Control Group^0 1^X2^02The following abbreviations are used in Table 3.2:X represents exposure of a group to an experimentalevent of which the effects were measured; and 0 refersto the measurement used (Campbell & Stanley, 1963; pp. 6)0 1 - the covariate (pretest)02 - the posttestX - the visual computer simulationX2 - the spreadsheet computer simulationAnalysis of Covariance (ANCOVA) The mean and standard deviation for pretest and posttest scores were calculated forboth experimental and control groups.To test the effect of the treatments, posttest results were analyzed by ANCOVA, usinga pretest as the covariate; the mean scores obtained on the posttest were adjusted for theinitial differences between the groups. Since the sample size was small and intact45classroom groups were being used to conduct this experiment, ANCOVA was consideredto be the appropriate statistical test (Campbell & Stanley, 1963).ANCOVA was repeated to investigate whether visual or active learning stylesmediated the experimental treatment.Threats to Internal ValidityCampbell and Stanley (1963) state that the nonequivalent control group designcontrols for the main effects of several threats to internal validity: history; maturation;testing; instrumentation; selection; and mortality.History is not likely to be such a threat since any event that might affect scores on thedependent variable would be experienced by both groups, and the duration of the studywas only one week. However, since all members of the experimental group were treatedin one session and all members of the control group were treated in another session, thepossibility of intrasession history (events unique to either session) affecting thedependent variable should be considered when interpreting the results of the study.Maturation is also not likely a threat since students were all at the same level in theprogram and the duration of the study was short. However, ages of the students vary,thus, the possible effects of students' past experiences related to the subject matter shouldbe considered in the interpretation of the data.Multiple testing is not likely to be a threat since both groups received the pretest; anyinfluence on student posttest achievement would be the same for both groups.Furthermore, the pretest and posttest were not the same test, they were quite different.Instrumentation is not likely a factor since both groups received the same tests and bothinstruments for both groups were scored by the same person. To attempt to control forany scorer bias on the part of the researcher, students used the last four digits of theirtelephone number instead of their name on the tests. In addition, subject numbers forboth groups were combined and listed randomly, and assignments for both groups were46combined and shuffled so that the researcher was unaware of specific treatmentapplication in relation to the assignment being graded and recorded.An analysis of variance revealed that there was a statistically significant difference inthe pretest achievement score means of the two groups in this study. This difference wascontrolled by using the pretest means scores as a covariate in the analyses of the data.Therefore, selection is not a threat to the internal validity of this study.Three students, two from the control group who missed the posttest and one from theexperimental group who missed the treatment, were eliminated from the study. Sinceboth treatment groups were expected to attend all sessions and the final sizes of thegroups were similar it seems reasonable to assume that deletion of the scores did notinfluence the results due to one group being more conscientious. Therefore, mortality isalso not a threat to the internal validity of this study.Regression, considered to be a possible source of concern (Campbell & Stanley,1963), is not a factor in this study as matching, to establish the pre-experimentalequivalence of the groups, was replaced by ANCOVA.Threats to External ValidityCampbell and Stanley (1963) suggest that potential threats to external validity of thenonequivalent control group design are the interaction of selection, maturation andhistory, interaction of testing and the treatment, interaction of selection and the treatmentand reactive arrangements.The questionable generalizability of the specific conditions which the experimentaland control groups have in common, such as: type of program enrolled in; stage in theprogram at which the treatments are applied; program entrance requirements; intelligenceof the subjects; and geographical region, should be considered in the interpretation of theresults.47The treatments for the study relate to subject matter specific to apparel designprograms. Attempts at generalization of the effects are, therefore, limited to subjectsenrolled in apparel design programs.This study was conducted during the natural course of events for intact classes.Students had been informed and had consented to participating in the experiment, butthey did not appear to be consciously aware of the event at the time that it was takingplace. It is therefore unlikely that reactive arrangements pose a threat to external validityin this study.InstrumentationEach of the three types of instruments used in this study: the thinking and learningstyles inventories; a pretest; and a posttest, are described in this section. Photocopies ofeach of the instruments administered to each of the participants in the study were retainedby the researcher. The original documents were returned to the participants when all ofthe components of the experiment were completed and scored.Thinking and Learning Styles Inventories The thinking and learning styles inventories, selected from the existing literature,were used to provide some insight into the relationship between the thinking and learningstyles of students enrolled in an apparel design program and their ability to cope withcosting exercises.The thinking styles inventory, Knowing Yourself - Right or Left (Wonder & Donovan,1984), and the learning styles inventory, Edmonds Learning Style Identification Exercise(ELSIE) (Reinert, 1976; ), are self-tests designed to assist in determining one's brainhemispheric dominance. The tests are based on the theory that either the righthemisphere of the brain, described as the visual-spatial, artistic side, or the lefthemisphere of the brain, considered to be the verbal, rational side, dominates the ways in48which one is able to think and learn most effectively. This "right brain - left brain"theory assumes that knowing which side of the brain is dominant for each individualstudent can assist teachers and learners in developing learning environments thatoptimize each individual's brain preference.Since both the thinking styles study (Wonders & Donovan, 1984, p. 31) and thelearning styles study (Reinert, 1976, p. 162) reported good psychometric properties of allthe test items, the two inventories were adopted for this study. These instruments wereadministered during regular class sessions on two different days, two weeks prior to theexperiment. The participants were able to score and interpret their own responses.Scores were recorded by the researcher.PretestA pretest was utilized to control for possible differences in mathematical abilitybetween the classes in this study. The pretest consisted of (See Appendix G) arithmeticmanipulations (seven questions; one mark each), simple word problems (five questions;one mark each) and the drawing of a floor plan for a production scheme (one question;three marks). There were 13 questions for 15 marks. The arithmetic manipulationsinvolved multiplication and division of decimals, conversion of fractions to decimals andidentification of largest fraction. Word problems included number of items, cost peritem, total cost and rate, time production. Students were given twenty minutes tocomplete the test. All of the students completed the test, taking from 14 to 20 minutes.The arithmetic manipulations and word problems for the pretest were modifications ofitems selected from the 1990 British Columbia Mathematics Assessment (Ministry ofEducation, 1990) for grades seven and ten. Since that study reported very goodpsychometric properties of all the items, the assembled pretest was considered to haveacceptable psychometric characteristics. It was administered to the pilot group to assist indetermining if it was an appropriate test for college level apparel design students. Scores49ranged from 10.5 to 13.5 out of 15 with a mean of 12.5. Students commented that theydid not think that the test was too easy. The researcher decided that the test would beappropriate to use in this study to assist in estimating the mathematical ability of thestudy participants in relation to the type of mathematical calculations needed inproduction costing. Some minor changes, mostly related to presentation, were made tothe pilot test.The pretest was administered two weeks prior to the experiment during a regular classsession and scored by the researcher.An Item Analysis Test was computed using the pretest scores (Davis, 1964 pp. 281-285). A comparison of the responses of high-scoring and low-scoring participantsshowed that more of the high-scoring participants correctly answered all but one item(See Appendix H). The results indicated that most of the items discriminated to someextent between students who have considerable arithmetic-reasoning ability and thosewho have little. Most of the items were, therefore, considered relevant to the propertiesmeasured by the test as a whole.PosttestThe posttest (See Appendix I) consisted of 10 word problems (one or two marks eachfor a total of twelve marks) and the drawing of a floor plan for a production scheme (onequestion; three marks) for a total of 15 marks. The questions in the posttest related to thecontent and type of arithmetic manipulations used in the computer exercises.The posttest was reviewed by four of the pilot study participants. Time did not allowfor a formal testing, and these students had used both simulations. Some minoradjustments were made to the posttest based on students' comments.The posttest was administered one week after the experiment during a regular classsession and scored by the researcher.50Scores on the posttest could vary based on the following two conditions: 1) whatstudents learned in the treatment; and 2) general mathematical ability. Sincemathematical ability could not be controlled by random assignment it was controlled byANCOVA using a pretest of mathematical ability.An Item Analysis Test was computed using the posttest scores (Davis, 1964 pp. 281-285). A comparison of the responses of high-scoring and low-scoring participantsshowed that more of the high-scoring participants correctly answered all of the items(See Appendix J). The results indicated that most of the items discriminated to someextent between students who have considerable knowledge or ability of the subject mattertested and those who have little. All of the items were, therefore, considered relevant tothe properties measured by the test as a whole.Pilot ProjectThe purposes of the pilot project were to: study the psychometric properties of the testinstruments, which had been assembled by the investigator, and to edit and alter them toproduce more reliable results in the main study; and to study the ease of use,comprehensibility, accuracy and students' feelings regarding the treatments.Twelve second-year Fashion Design and Technology students were invited toparticipate in the pilot project. This group of students had previously studied garmentproduction costing by selecting a standard sewing time from a chart that lists averagesewing times for standard garment styles. The time selected is then multiplied by a givenrate of pay per hour to arrive at a cost per unit. By this method the student does notactually divide the design into its individual components to determine the productioncost, therefore, missing the opportunity to experience how design details can affect costs.The pretest was administered to this group and scored by the researcher.Students were then asked to work with the computer spreadsheet simulation. Theyfound two programming errors, that the researcher was able to correct, and provided51positive feedback related to their feelings about using the simulation. They stated thatthey enjoyed doing calculations using the computer rather than paper and pencilexercises, and that the simulation allowed them to more easily see how costs are arrivedat.When the students were asked to work with the visual simulation they were asked tocompare it to the spreadsheet simulation. Again, the feedback was positive. Studentssaid that they preferred the visual simulation because it seemed more real. They also saidthat seeing the physical addition of more sewing machines on the screen for morecomplex designs gave them a better understanding as to why simplifying design detailscan often reduce production costs. Suggestions from the students for improvements tothe visual simulation included providing an on-screen calculator, allowing students to usethe computer spreadsheet to record their responses and having an animated icon torepresent a floor supervisor. It is the intention of the researcher to incorporate each ofthese ideas into a future version of the computer program.The posttest was reviewed by four of the pilot study participants.Data Collection and AnalysisCalculations for the analysis were conducted with the assistance of the researcher'sadvisor, Dr. D. Bateson at the University of British Columbia Computing Centre, usingthe SSPS-X statistical package.The level of significance used to accept the main treatment hypothesis and forrejecting the null hypothesis was set at .05, a commonly used probability level ineducational research (Christensen & Stoup, 1986). For the exploratory hypotheses, thesignificance level was set at .01 since no conclusions will be drawn, but only suggestionsfor further research will be made. In this case, it was considered that falsely rejecting atrue alternative hypothesis was of greater consequence than accepting a false nullhypothesis.52Overview of DataIndividual scores for all participants on both the pretest and posttest as well as theirthinking and learning styles inventory scores are listed in Appendices K and L.The means for the pretest and posttest scores and the posttest adjusted means werecalculated for both the experimental and control groups and for the entire sample (SeeTable 3.3) to provide an overview of the data prior to analyzing it for the effects of thetreatments.Table 3.3Summary Data of Pretest and PosttestPretest Means^Posttest Means^Posttest AdjustedMeansExperimental Group^9.09^10.06^10.43Control Group^11.25 10.78 10.40Note: Maximum Score 15The means for the thinking and learning styles inventories were calculated for both theexperimental and control groups (See Tables 3.4 and 3.5) to provide an overview of thedata prior to analyzing it for the effects of the treatments.Table 3.4Summary Data of Thinking Styles InventoryMeansExperimental Group^5.8Control Group^5.6MeansVisualization^Written Word^Listening^ActivityExperimental Group .52* .15 -1.10* -0.20Control Group .60* -0.20 -1.08* .80*53Note:^Scale for Table 3.4^1^5^9left balanced^rightbrain braindominance^dominanceTable 3.5Summary Data of Learning Styles Inventory* ± 0.5 is considered significantSummaryAn experiment to determine differences in student achievement between a visuallyinteractive computer simulation and a computer spreadsheet simulation on productionscheduling and costing was designed and implemented.Two further questions were also explored to investigate whether students' thinkingand learning styles mediated the experimental treatment.In this chapter the elements related to the design of the study were described. Thenext chapter will present the results of the study.54Chapter 4RESULTSThis chapter includes a brief description of the statistical procedures used to analyzethe data and deals with the disposition of the hypotheses stated in Chapter 3.Calculations for the analysis were conducted with the assistance of the researcher'sadvisor, Dr. D. Bateson at the University of British Columbia Computing Centre, usingthe SSPS-X statistical package.The level of significance used to accept the main treatment hypothesis and forrejecting the null hypothesis was set at .05, a commonly used probability level ineducational research (Christensen & Stoup, 1986). For the exploratory hypotheses, thesignificance level was set at .01 since no conclusions will be drawn, but only suggestionsfor further research will be made. In this case, it was considered that falsely rejecting atrue alternative hypothesis was of greater consequence than accepting a false nullhypothesis.Statistical ProceduresTo test for any effects of the treatments, posttest results were analyzed by analysis ofcovariance (ANCOVA), using the pretest as the covariate. This statistical procedureincreases the precision of the research analysis by removing the effects of initialdifferences that are considered important between the groups to identify more clearlywhether mean differences among groups were likely to have occurred by chance(Tabachnick & Fide11, 1983) or can be attributed to the experimental treatment. In thiscase the initial differences of major concern had to do with general mathematics ability.Therefore, the mean scores obtained on the posttest were adjusted for initial differences,measured by a test of general mathematics ability, between the groups. Since the samplesize was small and intact classroom groups were used to conduct this experiment,ANCOVA was considered to be the appropriate statistical test (Campbell & Stanley,1963).Disposition of HypothesesIn this section, the statistical tests of the hypotheses are described along withinterpretations of the findings.Research Ouestion 1: Treatment DifferencesHo :^The adjusted mean posttest scores for apparel design studentstrained using an object-based visually interactive computersimulation will not be significantly different on a test ofproduction costing and scheduling than for students trainedon a computerized spreadsheet simulation.= lix2 1= experimental (visual simulation) group1.t)(2 1 = control (spreadsheet simulation) groupH 1 :^The adjusted mean posttest scores for apparel designstudents trained using an object-based visually interactivecomputer simulation will be significantly higher on a testof production costing and scheduling than for studentstrained on a computerized spreadsheet simulation.An initial ANOVA on the unadjusted posttest scores indicated no significantdifference between the experimental and control groups [E(1, 50)=.85, ns]. However,because the groups differed on the pretest scores [E(1, 50)= 10.22,12 < .01] with the5556control group scoring significantly higher than the experimental group (means = 11.34and 9.09), ANCOVA was performed, controlling for pretest scores. This ANCOVAagain indicated no significant effect for group on the adjusted posttest scores [E(1, 49)=0.0]. The pretest covariate produced a significant beta of .30,12 < .05. The initialdifference between the groups justified the use of the pretest in the analysis of covariancefor testing the hypotheses in this study.An increase from pretest to posttest for the experimental group was expected andachieved, but the increase was not statistically significant (9.09 to 10.06). The nullhypothesis was accepted because the probability of this result occurring is greater thanthe alpha level set at .05 and can therefore be attributed to chance.Two further questions, with the alternative hypotheses being non-directional since thedirection of the results were unpredictable, were also tested. Since research question onehas a directional H1 a one tailed test was used. However, for questions two and three, atwo tailed was used.Research Ouestion 2: Visual Learning StyleHo :^The adjusted mean posttest scores for apparel designstudents identified as higher visual learners willnot be significantly different on a test of productioncosting and scheduling from the lower visual learner group.=11,= higher visual learners groupvL i = lower visual learners groupH I :^The adjusted mean posttest scores for apparel designstudents identified as higher visual learners willbe significantly different on a test of productioncosting and scheduling from the lower visual learners group.µV1-1 1 /1 VI:Students were divided into two groups on the basis of a median split of their scores onthe visual learning sub scale of the learning styles inventory. The high and low visuallearners were split approximately equally between experimental and control groups. Toinvestigate whether a visual learning style mediated the experimental treatment, thisfactor was introduced into the ANCOVA reported above as a second factor. Table 4.1shows that while neither the treatment group nor the visual learning style main effectswere significant, the interaction of the experimental treatment group by visual learningstyle was significant.Table 4.1Visual Learners SubgroupANCOVA on Posttest ScoresControlling for Pretest ScoresSource of Variation SS DF MS F pPretest Covariate 29.18 1 29.18 3.97 .05Group .24 1 .24 .03 .86Visual 2.54 1 2.54 .35 .56Group by Visual 20.50 1 20.50 2.79 .10*Within Cells 338.09 46 7.3557Figure 1 indicates that while low visual learners seemed to perform better on thecontrol treatment relative to the high visual learners (controlling for pretest scores), high58visual learners in the experimental treatment group appeared to do better compared withthe low visual learners in the experimental treatment.ControlExperimental Visual Low Visual HighFigure 1Visual Learners SubgroupPosttest Adjusted Mean ScoresResearch Question 3: Active Learning StyleHo :^The adjusted mean posttest scores for apparel designstudents identified as higher active learners willnot be significantly different on a test of productioncosting and scheduling from the lower active learners group.= 11 Al:= higher active learners groupP.AL: = lower active learners group59H I :^The adjusted mean posttest scores for apparel designstudents identified as higher active learners willbe significantly different on a test of productioncosting from the lower active learners group.*11 Al:Students were divided into two groups on the basis of a median split of their scores onthe active learning sub scale of the learning styles inventory. The high and low activelearners were split approximately equally between experimental and control groups. Toinvestigate whether an active learning style mediated the experimental treatment, thisfactor was introduced into the ANCOVA reported in the first null hypothesis as a secondfactor.Table 4.2 shows that while none of the effects were significant, the interaction of theexperimental treatment group by active learning style might indicate a trend.Table 4.2Active Learners SubgroupANCOVA on Posttest ScoresControlling for Pretest ScoresSource of Variation SS DF MS F pPretest Covariate 37.04 1 37.04 4.91 .03Group .13 1 .13 .02 .90Visual .93 1 .93 .12 .73Group by Visual 15.09 1 15.09 2.00 .16Within Cells 347.38 46 7.55ID Control0 ExperimentalActive Low Active High11.51110.5109.560Figure 2 indicates that while low active learners seemed to perform better on thecontrol treatment relative to the high active learners (controlling for pretest scores), highactive learners in the experimental treatment group seemed to perform better comparedwith the low active learners in the experimental treatment.Figure 2Active Learners SubgroupPosttest Adjusted Mean ScoresIt should be noted that the relationship between the visual learning style and theactivity learning style was uncorrelated [x(49)= -.10, 12 > .48]Thinking and Learning Styles InventoriesThinking Styles InventoryFor this brain preference indicator test, on a scale from one to nine, a person with ascore of four to six is considered mixed dominant, a score of one is considered left brain61dominant and a score of nine is considered right brain dominant. The mean score for theexperimental group was 5.8 with nearly 33% of the students scoring more than six. Themean score for the control group was 5.8 and, again, nearly 33% of the students scoredmore than six. The results suggest that the members of both groups are mixed dominant,but there is a tendency toward right brain dominance.Learning Styles InventoryFor this inventory a mean score of ± 0.5 is considered significant. For the"visualization" category, the mean scores for both the experimental (.52) and control(.60) groups were significant in a positive direction. For the "listening" category, themean scores for both the experimental (-1.10) and control (-1.08) groups were significantin a negative direction. For the "activity" category, the mean score for the experimentalgroup (.-0.20) was not significant but, for the control group, a mean score of .80 wassignificant in a positive direction. For the final category, "written word" the meanscores for both the experimental (.15) and control (.-0.20) groups were not significant.Since the visualization category, considered to be a right brain activity, wassignificant in a positive direction, and the listening category, considered to be a left braincategory, was significant in a negative direction, the results suggest that the members ofboth groups show a tendency toward right brain dominance.SummaryThe researcher realized that ANCOVA was the appropriate statistical test to analyzethe data as it was shown that the initial differences of the two groups was statisticallysignificant.Students in the group that received the visual computer simulation treatment achieveda higher adjusted mean score on a test of production costing and scheduling, although not62statistically significant, than the students who received the computerized spreadsheettreatment.The analyses indicate that there may be a directional relationship between studentsidentified as visual learners who used the visual computer simulation and achievement ona test of production costing and scheduling as there was a significant increase in adjustedposttest scores.The analyses also indicate that there may be a trend in students identified as activelearners who used the visual computer simulation and achievement on a test ofproduction costing and scheduling as there was an increase in adjusted posttest scores.An informal analysis of the data from the thinking and learning styles inventoriessuggests that both groups of students tend to be more right brain or visually oriented intheir thinking and learning styles.63Chapter 5SUMMARY, CONCLUSIONS, REFLECTIONS ON THE RESEARCHAND IMPLICATIONSThe purpose of the study, the procedures involved and the results are summarized inthis chapter, followed by conclusions, some reflections on the research, implications forfuture research and implications for instruction.Summary of the StudyThe purpose of this study was to explore ways in which object-based visuallyinteractive computer simulation is an effective learning environment in which to teachapparel production management to apparel design students who are more inclined towardthe artistic than the mathematical. A review of the literature suggests that object-basedvisually interactive computer simulation provides a positive learning environment, hasthe potential to produce dynamic, intrinsically captivating simulations and can visuallyreplicate the environment in which graduates will work. Therefore, it was hypothesizedthat students receiving an object-based visually interactive computer simulationtreatment would score higher on a test of production costing and scheduling than studentsreceiving a computer spreadsheet simulation treatment. Data were collected andanalyzed to test this hypothesis. Two further questions were also examined:1. Will there be a significant difference in the adjusted mean posttestscores for apparel design students identified as higher visuallearners and achievement on a test of production costing andscheduling compared to the lower visual learners group?2. Will there be a significant difference in the adjusted mean posttestscores for apparel design students identified as higher activelearners and achievement on a test of production costing andscheduling compared to the lower active learners group?64The Nature of the StudySince intact classes were randomly assigned to treatments, the nonequivalent controlgroup quasi-experimental design approach as outlined by Campbell and Stanley (1963,pp. 47-50) was used for this study.Null hypotheses were formulated from the research hypothesis and the additionalquestions posed for the study, and were treated with an analysis of covariance(ANCOVA).At the beginning of the experiment, instruments to identify students' thinking andlearning styles, and a pretest to control for possible differences in mathematical abilitybetween the two classes in this study, were administered to all subjects. Two weekslater, subjects in the experimental group were assigned the visual computer simulationexercise while subjects in the control group were assigned the computer spreadsheetexercise. Each group was allowed one-and-one-half hours to complete the assignedexercise. An achievement test pertaining to the mathematical content of the computerexercises and drawing of a production scheme, was administered to both groups as aposttest one week following the experiment.To measure the treatment effects, posttest results were analyzed by ANCOVA using apretest as the covariate; the mean scores obtained on the posttest were adjusted for theinitial differences between the groups. ANCOVA was repeated to investigate whethervisual or active learning styles mediated the experimental treatment.Summary of ResultsThe level of significance used to accept the main treatment hypothesis and forrejecting the null hypothesis was set at .05 and for the exploratory hypotheses, thesignificance level was set at .01 since no conclusions will be drawn, but only suggestionsfor further research will be made.65ANCOVA was the appropriate statistical test to analyze the data as it was shown thatthe initial differences of the two groups was statistically significant.An increase from pretest to posttest adjusted mean scores for the experimental groupwas expected and achieved, but the increase was not statistically significant (9.09 to10.06). The null hypothesis was accepted because the probability of this result occurringis greater than the alpha level set at .05 and can therefore be attributed to chance.The analyses indicate that there may be a directional relationship between studentsidentified as visual learners who used the visual computer simulation and achievement ona test of production costing and scheduling as there was a significant increase in adjustedposttest scores.The analyses also indicate that there may be a trend in students identified as activelearners who used the visual computer simulation and achievement on a test ofproduction costing and scheduling as there was an increase in adjusted posttest scores.An informal analysis of the data from the thinking and learning styles inventoriessuggests that both groups of students tend to be more right brain or visually oriented intheir thinking and learning styles.ConclusionsIn conclusion, this section will describe the limits of the study, examples of thelearning theories outlined in Chapter 2 addressed in the experimental treatment (visualsimulation) and uses for the visual simulation in apparel design programs.Limits of the StudyA number of factors encountered in the research design and procedures might haveaffected the results obtained in this study. First, the considerably large differencebetween the two groups mean scores on the pretest was unanticipated and may need to beconsidered in future administrations of the treatment. Students in the visual computer66simulation group obtained a mean score of 60.6%, compared to the control group meanof 75% on the pretest, which consisted of questions selected from a mathematicsassessment for grades seven and ten. The results suggested that the mathematical abilityof the experimental group was low and it could, therefore, be expected that any stimuluspresented only once is unlikely to have a large effect. However, the mean score for theexperimental group did increase on the posttest, which was a more difficult test than thepretest because it was designed for college level students. This result suggests that thevisual computer simulation could have been a positive learning environment for many ofthe participants, but that students need to be exposed to the stimulus for several sessions.This is consistent with Atkinson and Burton's (1991) findings that students who used acomputer simulation the most performed better on achievement tests than students whohad little practice time.Secondly, it could be argued that there was very little difference in the visual screenpresentation between the experimental and control treatments since both simulationsdisplayed the same spreadsheet. The researcher choose to have the control group use acomputerized spreadsheet rather than a paper and pencil spreadsheet so that the "help"option incorporated into the simulations and learning to use the computer, especially themouse, would not be considered intervening variables since both groups worked with thesame conditions.Thirdly, the effects on the results of the limiting factors associated with the visualcomputer simulation, since the programming language used was a beta-test model, thesmall sample size available for this experiment, and the use of intact classroom groupsrather than randomized groups, are unknown. Therefore, the results are onlygeneralizable to the participants in the study .Finally, the effects on the results of potential threats to the internal and externalvalidity of the study identified in Chapter 3: intrasession history; students' pastexperiences related to the subject matter; subject specific treatments; and the questionablegeneralizability of the specific conditions which the experimental and control groupshave in common, are also unknown, thus, contributing to the limited generalizability ofthe study.Learning Theories and The Experimental TreatmentThis section will describe how the visual computer simulation used in this studyattempted to address aspects of the learning theories that were outlined in Chapter 2.If simulations allow one to build on their previous knowledge, then it is necessary toensure that each student has an appropriate knowledge base to build from prior to using asimulation (Goodyear, 1991; Papert, 1991; Stead, 1990). It was felt that the studentswho used the visual computer simulation in this study had an appropriate knowledge basesince the simulation was administered after the students had been assigned a series ofreadings, had spent two, two-hour sessions discussing matters related to mass productionand had viewed two video tapes on the topic. Also, since all of the students knew how tosew a basic skirt, there were a number of recognizable objects on the computer screen.Papert (1991), Lawler et al (1986) and Goodyear (1991) advocated that supportmaterials and the intervention of a facilitator are necessary to assist learners as they workwith a computer program. For this study, students were provided with a demonstration,some written information to use as a reference and the teacher was present throughout theadministration of the simulations.The visual simulation encouraged students to be actively involved in their learningexperience by requiring that they make selections in order to acquire the data they neededto solve problems and to run the simulation.The visual simulation encouraged students to use both hemispheres of their brain; thespreadsheet and graph referred to mathematical elements and the user was required tomake several computations, thus, addressing the left side of the brain, and the pictorialsewing machines with the garment bundles moving through the system gave a realistic,6768visual representation of a manufacturing environment, thus, addressing the right side ofthe brain.Many of the early advantages of computer-based learning were incorporated in thevisual simulation. Students can use the simulation individually, receive immediatefeedback to their responses, remediation is provided, the degree of difficulty isprogressive and the amount of practice provided is infinite. Also, repetition is provided,but the data is always different so that the students have to compute each response. Theintended use of the simulation is that it supplement other forms of instruction. Thissupports Bennet's (1991) and Spencer's (1991) findings that simulation was mosteffective when used as a supplement to other forms of instruction.A number of the studies cited stated that colour, graphics and animation should beused to illustrate important features of the material presented (Baek & Layne, 1988;Spencer, 1991; Thorell & Smith, 1990). The simulation is a simple pictorialrepresentation of a complex setting. The only aspect of the factory floor that is displayedon the computer screen is the layout of sewing machines for a specific garment style.The sewing machines are simple line drawings. Colour and animation are used to showthe garment bundles moving through the system. A graph is provided for the user toquickly assess the number of garments being produced each hour. The simulationreplicated an environment in which apparel design students will be expected to work.Determining the transferability of the learning gained from the use of the simulation wasbeyond the scope of this study. It was anticipated that the visual simulation would beintrinsically motivating due to the use of colour, graphics and animation. The simulationattempted to address Malone's (1984) elements of an intrinsically motivatinginstructional environment by incorporating: challenge, with the use of progressivelymore difficult problems to solve; fantasy, with the use of representational objects thatreplicated a small portion of a large system; and curiosity with three garment styles, oroptions, for the user to choose to work with.69The visual simulation is only a prototype and as yet does not provide an opportunityfor the teacher or the users to alter the parameters, or allow the users to create their ownmodels. These components will be considered in future versions of the simulation.Uses for the Visual Simulation in Apparel Design Programs The visual simulation used in this study should provide a practical solution forinstructors who need resources to teach apparel production management to students whotend to be more artistic than mathematical. It is an extensible prototypical computersimulation that can be used in the classroom to provide a flavour of the topic, especiallyfor programs that are not able to offer courses dedicated to production management.The visual simulation should also contribute to the implementation of computer-assisted learning environments in apparel design programs. It could be incorporated intothe curriculum in a variety of ways; a few suggestions are offered here. First, dependingon the number of computers available, students could either run the simulationindividually, in pairs or in small groups. If only one computer is available, the teachercould run the simulation, using an overhead projector so that all of the students could seethe computer screen and either use the simulation as a demonstration or as a groupproblem solving session. Students could then use the simulation individually, either inclass time or on their own time. Regardless of the way that the simulation is run, it couldalso be used as a point of departure for students to plan and build their own factory floorlayouts on paper. It could also be used as a point of departure for further discussion ofproduction costing and scheduling.The visual simulation could be used to fulfill the need of textile and clothinginstructors for new and better ways of enhancing students' visual thinking andcommunication skills as identified by O'Riley (1988).In summary, the visual computer simulation appears to be a positive teaching learningstrategy that can be used in the classroom as an integral component of the curriculum as a70supplement to instruction on apparel production management, should be presented morethan once during class time and should be available for students to use on their own time.Reflections on the ResearchAn exciting opportunity to collect qualitative data arose while the students wereworking with the computer simulations. Students were required to work in pairs whichallowed them to problem solve collaboratively through spoken language and gestures.Observation and record taking were not a part of the design of this study. However, thelaboratory setting unexpectantly freed me from providing some of the usual individualassistance required because the students, with help from their partners, resolved theirown operational problems, such as how to get the mouse to work and what to do next.Also, I was able to overhear and see some of what the students were thinking while theyworked through the exercises. Consequently, I decided to use this opportunity to recordsome of the students' comments and actions.Three incidents arising from the experimental (visual simulation) setting that could beused to develop a conceptual framework for part of a future study that investigates whythis medium may be a rich teaching format that guides learning, are described here. Thefirst scenario illustrates active involvement in the learning process, the second relates tomath phobia and the third is an example of students' responses following the experiment.Students used the visual simulation by selecting options from menus with a mouse.When a garment style was selected, the program displayed a layout of a factory floorwith sewing machines representing the steps in the construction of the garment. Eachsewing machine had two gauges which rose and fell with the flow of garment pieces inand out of the sewing station. I noticed that Jill, one of the students, was imitating theaction of the two gauges attached to the on-screen sewing machines. With elbows bent,she alternated lifting each arm up and down as she swayed her body from side-to-side in71time with the rise and fall of the flow of pieces. Jill appeared to be actively involved inher learning experience. Watching her reminded me of Papert's (1980) belief thatstudents become the objects they study. In this instance, Jill was the garment pieces.Another student, Anne, arrived late and was clearly flustered. She stated that she wasembarrassed to be late, was intimidated by having to use a computer and disliked subjectmatter related to math. She refused the opportunity for a demonstration, saying that sheshould take the responsibility to catch up on her own. It soon appeared that she wasunable to concentrate on the written instructions. She accepted the second offer of ademonstration. Twenty minutes later, she was moving through the exercise rapidly, witha smile on her face. Not having a partner, she discussed the activity with the twostudents sitting at the computer next to her. Anne later told me that she felt sheunderstood the math involved in the exercise, a rare experience for her. Her test scoreswent from 5 out of 15 on the pretest to 13 on the posttest.Other students indicated that they were not strong in mathematics, but the visualsimulation helped make the process more real to them; the calculations made sense:• Ruby, "Now I know exactly how the price of a garment is arrived at.";• Andrew, "I hadn't realized before that every minute of sewing time can make adifference to the cost.";• Rod, "I'm enjoying doing math using this program."; and• Theresa, "I wish math had been taught this way in high school."In a regular classroom setting the students in the experimental group are generallypassive, requiring creative approaches from their instructors to motivate them toparticipate more actively. Worthy of note is that during the class following the visualcomputer simulation exercise, a number of students in the experimental group askedquestions related to garment production which prompted voluntary discussions involvingseveral students. Their questioning was more probing than questioning from the controlgroup or from previous groups of students studying the unit on production management.72Sherry asked, for example, "Isn't the rate of production only as fast as the slowestmachine operator?" This question led to a discussion on how the average times for eachsewing operation are arrived at and the concept of linear versus parallel processing.Subsequent questions led to a discussion that went beyond the scope of the simulation; tohow bottlenecks, machine breakdowns and absenteeism will influence the productioncost of garments.Throughout the experiment both the control and experimental groups were deeplyengrossed in their respective computer exercise. Several times when the researcherdirected a question to a student, the student just looked up for a moment and smiled ornodded and immediately returned to work. All of the students in both groups were seenattempting to answer the questions asked in the simulation before they clicked on thecells in the computer spreadsheet that would reveal the correct responses. In most casesstudents were able to work out the correct response from the assistance provided in thesimulation, but when they did not answer a question correctly they referred back to thehelp section and tried again.Although the software was the learning pedagogy here, the process of the students'collaboration became a point of interest to me. The concept of collaborative learningusing computers has recently stimulated a number of researchers to "pursue . . . researchprojects that study collaborative learning and other cognitive processes in situ" (Jackson,1990, p. 65). Advantages of collaborative learning include maximizing the use ofhardware and software by sharing, and students learning to help each other so that theycan work more independently from the teacher. A deeper use of combining computersand collaborative learning techniques might be found in students using computer modelsto jointly discover concepts and meanings through discussion and criticism of each otherspoint of view (O'Malley & Scalon, 1990). The findings here could be used as a base forthe development of a conceptual framework on collaborative learning using computers aspart of a future study.Feedback from the students in both groups was overwhelmingly positive. Certainlythe novelty of the medium can be attributed to the interest and enthusiasm expressed bythe participants. The enthusiasm displayed by the students and the surprisingly deepnature of the discussion that followed the experiment convinced me that the visualcomputer simulation was worth the effort and has considerable future potential.Reflecting on the research has raised a number of questions for me that will bediscussed in the following section on implications for future research.Implications for Future ResearchBased on the results of the study and the reflection on the research, some implicationsfor future research are suggested here:1. that the visual simulation be tested by other apparel design instructors to validate itsusefulness in presenting the subject material, ease of use, effectiveness and toprovide recommendations for enhancing the software program;2. that a similar experiment be carried out with a larger sample size and randomizationof subjects into control and experimental groups to increase the statistical power ofthe study;3. that any further research using a visual computer simulation take place over aminimum of one semester and that ways to measure transferability of the knowledgeand skills gained, be addressed.4. that further research be carried out to investigate how students' individual thinkingand learning styles can best be addressed using a visual computer simulation.5. that a framework be developed to evaluate the qualitative aspects of the use of themedium that emphasizes both cognitive and behavioral aspects of instruction using avariety of assessment techniques; and6. that further research be carried out that looks more deeply into the nature of object-based simulations. The following are a few of the questions that could be considered:73746.1 What does the learner learn from using the visual simulation used in this study;concepts related to production costing and planning, how to do mathematicalcalculations, or both?6.2 Can using a visual computer simulation change the definition of learning for thelearner, and if so, how?6.3 Can a person become a better thinker as a result of using a visual simulation?6.4 Does interaction with a visual computer simulation alter the users' perspectiveon the world around themselves?Implications for InstructionBased on the results of the study and the reflection on the research, some implicationsfor instruction are also suggested here.The extent of this study was to provide a prototypical solution for instructors who needresources to teach apparel production management. Future considerations for the designof the software include: allowing the teachers and the students to alter the parameters;provisions for the effects on a production system due to breakdowns, bottlenecks andabsenteeism; opportunities for the users to experiment with a variety of productionsystems; and an environment in which students can research and create their own modelsof a factory floor. Interactive video should be incorporated in the development of futuresoftware.An object-oriented computer language should be used to develop a framework forteachers and students to access and manipulate so that they are actively involved in thedecision making in the development of their own models without the distraction ofactually having to learn the programming language.Instructors in clothing design programs should consider how the curriculum can beorganized to maximize the use of microcomputer technology as an integral part of thecurriculum. The challenge will also require consideration for the incorporation of a75variety of teaching/learning strategies, such as collaborative learning, and the changingrole of the instructor from that of a lecturer to a facilitator. The effort will be well worthit. Since, as Alan Kay (1984) said, "As in all the arts a romance with the material mustbe well under way." (p. 9)76REFERENCESAckerman, E. (1991). From decontextualized to situated knowledge: Revisiting Piaget'swater-level experiment. In I. Harel & S. Papert (Eds.). Constructionism (pp. 269-294). Norwood, NJ: Ablex.Atkinson, M., & Burton, J. (1991). Measuring the effectiveness of a microcomputersimulation. Journal of Computer-Based Instruction, La, 63-65.Baek, Y, & Layne, B. (1988). Color, graphics, and animation in a computer- assistedlearning tutorial lesson. Journal of Computer-Based Instruction,11 131- 135.Bell, P., & O'Keefe, R. (1987). Visual interactive simulation - History, recentdevelopments, and major issues. Simulation, 42, 109-116.Bennett, J. (1991, August). Effectiveness of computers in the teaching of secondarymathematics: Fifteen years of reviews of research. Educational Technology, pp. 44-48.Borne, I., & Girardot, C. (1991). Object-oriented programming in the primaryclassroom. Computers and Education, h, 93-98.Bracey, G. (1987, Nov./Dec.). Computer-assisted instruction: What the research shows.Electronic Learning, pp. 22-23.Bresler, L., & Walker, D. (1990). Implementation of a computer-based innovation: Acase study. Journal of Computer-Based Instruction,  11, 66-72.CACI. (1987). SIMFACTORY with animation [Computer program manual]. LosAngeles, CA: Author.Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs fo:research. Boston, MA: Houghton Mifflin.Christensen, L., & Stoup, C.M. (1986). Introduction to statistics for the social and behavioral sciences. Belmont, CA: Wadsworth.Davis, F. (1964). Educational measurements and their interpretation. Belmont, CA:Wadsworth.Dewey, J. (1963). Experience & education. New York: Collier Books.(Original works published 1938)diSessa, A. (1986). Artificial worlds and real experience. Instructional Science, .11,207-227.diSessa, A., & Abelson, H. (1986, September). Boxer: A reconstructible computationmedium. Communications of the ACM, pp. 859-868.Edwards, B. (1979). Drawing on the right side of the brain. Los Angeles, CA: J.P.Tarcher.77Fenton, J., & Beck, K. (1989). Playground: An object oriented simulation system withagent rules for children of all ages [Summary ]. In N. Meyrowitz (Ed.). Object-Oriented Programming: Systems. Languages and Applications Conference Proceedings. (pp. 123-137). New Orleans, LA: ACM Press.Finzer, W., & Gould, L. (1984, June). Programming by rehearsal. Byte, pp. 187-210.Forney, J., Rosen, D., & Orzenchowski, J. (1990). Domestic versus overseas apparelproduction: Dialogue with San Francisco-based manufacturers. Clothing andTextiles Reasearch Journal, $(3), 39-44.Fraser, A., Christman, L., Else, J., Hughes, F., & Glock, R. (1989). TC 2: Implicationsfor university clothing/textile curricula [ Summary ]. In C. Nelson (Ed.).Proceedings of the Association of College Professors of Textiles and Clothing.Monument, CO: ACPTC.Friese, R. (1986). The future of the apparel industry. Readywear,  9_7(2), 25-28.Gilabert, R., & Gavalda, J. (1990). Evaluation of a technique to introduce the use ofsoftware in chemical engineering. British Journal of Educational Technology,  21,52-59.Glock, R.& Kunz, G. (1990). Apparel manufacturing sewn products analysis. NewYork: MacMillan.Goldman-Segall, R. (1991). Three children, three styles: A call for opening thecurriculum. In I. Harel & S. Papert (Eds.). Constructionism (pp. 235-268).Norwood, NJ: Ablex.Goldman-Segall, R. (1991a). A multimedia research tool for ethnographic investigation.In I. Harel & S. Papert (Eds.). Constructionism, (pp. 467-497). Norwood, NJ:Ablex.Goodyear, P. (1991). A knowledge-based approach to supporting the use of simulationprograms. Computers and Education, 1, 99-103.Gowin, B. (1981). Educating. London: Cornell University Press.Gredler, M. (1986, April). A taxonomy of computer simulations. Educational Technology, pp. 7-12.Guild, P., & Garger, S. (Eds.). (1985). Marching to different drummers.Alexandria, VA: Association for Supervision and Curriculum Development.Guimaraes, A., & Dias, R. (1992). Some considerations for the design andimplementation of an instructional HyperCard-based simulation. Simulation/Games for Learning, 22, 16-31.Hallem, J. (1990). A new breed: the QR manager. Apparel Industry,  5E3), 32-38.Harel, I. (1988). Software design for learning: Children constructions of meanings for fractions and Logo programming. Unpublished doctoral dissertation, MIT MediaLaboratory, Cambridge, MA.78Harel, I., & Papert, S. (1991). Software design as a learning environment. In I. Harel &S. Papert (Eds.). Constructionism (pp. 41-84). Norwood, NJ: Ablex.Harlock, S. (1989). Prospects for computer integrated manufacturing (CIM) in theclothing industry. International Journal of Clothing Science and Technology,  1(2),17-24.Hartley, R., & Lovell, K. (1984). The psychological principles underlying the design ofcomputer-based instructional systems. In D.F. Walker & R.D. Hess (Eds.),instructional software principles and perspectives for design use  (pp. 38-56).Belmont, CA: Wadsworth.Hill, E. (1992, March). Flexible manufacturing systems, part 2. Bobbin, pp. 70, 71-78.Holloway, K., & Ledwith, B. (Eds.). (1989). Proceedings of On the Cutting Edge Computer Applications in Clothing Design for People with Special Needs.  Saratoga,CA: West Valley Community College.Hudson, P. (1989). Guide to apparel manufacturing. Greensboro NC: MEDIApparel.Im, Y. (1991). Apparel CAD/CAM: Designing the future Summary ]. In E. Horridge(Ed.). Proceedings of the International Textile and Apparel Association. Inc.Monument, CO: ITAA.Jackson, I. (1990). Children's software design as a context for studying collaboration.In I. Harel (Ed.). Constructionist learning: a 5th anniversary collection of papers  (pp.65-82). Cambridge, MA: MIT Media Library.James, E. (1986). Computer-based teaching for undergraduates: Old problems and newpossibilities. Computers and Education, 2, 267-272.Kay, A. (1984). Computer Software. In Scientific American Book (Ed.), Computer Software (pp. 4-9). New York: Freeman and Co.Kay, A. (1990). User interface: A personal view. In B. Laurel (Ed.). The art of human-computer interface design (pp. 191-207). Menlo Park, CA: Addison Wesley.Kay, A. (1991, September). Computers, networks and education. Scientific American.pp. 138-149.Knoll, D. (1989). Computers in the study of clothing and textiles: Current use andfuture trends (Doctoral dissertation, University of Arkansas, 1989). Dissertation Abstracts International, 511, 1898.Laurel, B. (Ed.). (1990). The art of human-computer interface design.  Menlo Park: CA:Addison Wesley.Lawler, R., du Boulay, B., Hughes, M., & Macleod, H. (1986). Cognition and computers: Studies in learning. West Sussex, England: Ellis Horwood.Legault, L. (1992, July/August). A step toward technology with computer simulation.The Canadian Apparel Manufacturer,  pp. 34, 35,39.79Lockhard, J., Abrams, P., & Many, W. (1990). Microcomputers for education.  U.S.A.:Harper Collins.Malone, T.W. (1984). Toward a theory of intrinsically motivating instruction. In D.F.Walker & R.D. Hess (Eds.), Instructional software principles and perspectives for design use (pp. 68-94). Belmont, CA: Wadsworth.Martin, B., & Dixon Hearne, J. (1990, January). Transfer of learning and computerprogramming. Educational Technology, pp. 41-44.McCaskey, M. (1989). Assessing the affect of computer-aided instruction. Journal of Studies in Technical Careers, 11, 119-130.Miller, P. (1991). Developing a cost sheet for sewn products using lotus software[ Summary ]. In E. Horridge (Ed.). Proceedings of the International Textile and Apparel Association. Inc.  Monument, CO: ITAA.Ministry of Education. (1990).  British Columbia mathematics assessment for grades 7 and 10. Victoria: author.Minsky, M. (1986). Society of Mind. New York: Simon & Schuster.Montessori, M. (1966). Dr. Montessori's own handbook.  Cambridge, MA: RobertBently. (Original work published 1914)Morton, D. ( in press ). Using active objects to manage groups of objects in adistributed environment. Object-Oriented Programming: Systems. Languages and Applications Conference Proceedings. New Orleans, LA: AMC Press.Novak, J., & Gowin, B. (1986). Learning how to learn.  New York: CambridgeUniversity Press.Oxford illustrated dictionary. (1976). London: Oxford University Press.O'Malley, C., & Scanlon, E. (1990). Computer-supported collaborative learning:problem solving and distance education. Computers and Education, 11, 127-136.O'Riley, A. (1988). Development and testing of a computer simulation in apparel design. Unpublished master's thesis, Iowa State University, Ames, IA.Page, T. Jr., Berson, S., Cheng, W., & Muntz, R. (1989). An object-oriented modellingenvironment [ Summary ]. In N. Meyrowitz (Ed.). Object-Oriented Programming: Systems. Languages and Applications Conference Proceedings (pp. 287-296). NewOrleans LA: AMC Press.Papert, S. (1980). Mindstorms: children. computers. and powerful ideas.  New York:Basic Book.Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.).Constructionism (pp. 1-12). Norwood, NJ: Ablex.Peck, J., & Pargas, R. (1991, June). Simulating the future. Bobbin, pp. 16, 18.80Premachandra, I. (1991). A spreadsheet program for learning some aspects of projectmanagement. Computers and Education, .1, 235-242.Rabolt, N. (Ed.). (1990). Computer applications in textiles & clothing ACPTC special publication #2. Monument, CO: ACPTC.Randhawa, B., & Hunt, D. (1986). Computers and computer literacy in contemporarypsychological, socio-economic and educational context. In D. Harper & J. Stewart(Eds.). Run: Computer education (pp. 77-82). Monterey CA: Brooks/Cole.Reinert, H. (1976). One picture is worth a thousand words? Not necessarily! ihp_Modern Language Journal. LX(4). 160-168.Rieber, L., Boyce, M., & Assad, C. (1990). The effects of computer animation onadult learning and retrieval tasks. Journal of Cotnputer-Based Instruction, ,11, 46-52.Riley, D. (1990). Learning about systems by making models. Computers and Education,I5, 255-263.Rogers, C. (1969). Freedom to learn. Columbus, OH: Charles E. Merrill.Rose, F. (1987, November). Pied piper of the computer. New York Times Magazine,pp. 56, 60, 140, 152.Sachter, J. (1991). Different styles of exploration and construction of 3-D spatialknowledge in a 3-D computer graphics microworld. In I. Harel & S. Papert (Eds.).Constructionism (pp. 335-364). Norwood, NJ: Ablex.Scheres-Koch, I. (1988). Keeping ahead through technology management. Readywear,lia(2), 32-35.Sheldon, G. (1988). The impact of technology on apparel designer training. Clothing and Textiles Research Journal, (4), 20-25.Shelton, L. & Dickerson, K. (1989). Global textile trade: implications for industry,government and education. Journal of Home Economics, /31(4), 46-52.Smith, P. (1986, June). Low-cost simulations: The impossible dream revisited.Educational Technology, pp. 35-38.Smith, R. (1986). The Alternate reality kit. IEEE Expert, pp. 99-115.Spencer, K. (1991). Modes, media,and methods: The search for educationaleffectiveness. British Journal of Educational Technology, 22, 12-22.Staff. (1987). Quick Response - CAD/CAM/CIM - unit production systems.Readywear, 22(2), p. 28.Stead, R. (1990). Problems with learning from computer-based simulation: A case studyin economics. British Journal of Educational Technology, 21, pp. 106-117.Steed, M. (1992). Stella, a simulation construction kit: Cognitive process andeducational implications. Journal of Computers in Mathematics and Science Teaching, fl, 39-52.Steinhaus, N. (1989). Flat pattern design - an industry/classroom connection[ Summary]. In C.N. Nelson (Ed.). Proceedings of the Association of College Professors of Textiles and Clothing. Monument, CO: ACPTC.Synaptec Holdings Ltd. (1990). AUDITION [Computer program]. Vancouver, B.C.:Author.Systems Modeling Corp. (1988). CINEMAHLoutgamiseen before! Sewickley, PA: Author.Tabachnick, B. & Fidell, L. (1983). Using multivariate statistics. San Francisco,CA: Harper & Row.Tennyson, R. (1990, June). A proposed cognitive paradigm of learning for educationaltechnology. Educational Technology, pp.16-19.Thomas, D. (1989, March). What's in an object? Byte, pp. 229.Thorell, L., & Smith, W. (1990). Using computer colors effectively: An illustrated reference. Englewood Cliffs, NJ: Prentice Hall.Tray, A. (1986). Joseph Gerber: Apparel's Thomas Edison. American Fabric & Fashions, 114(1), 118-122.Turkle, S., & Papert, S. (1991)). Epistemological pluralism and the revaluation of theconcrete. In I. Harel & S. Papert (Eds.). Constructionism (pp. 161-192). Norwood,NJ: Ablex.Turner, J. (1990). The computer is the ultimate weapon. Apparel, 14(2), 30-31.Walker, D. (1983, May). Reflections on the educational potential and limitations of microcomputers. Phi Delta Kappan, pp. 103-107.Walsh, W. (1989, September). Apparel Management Letter. ( Available from[American Apparel Manufacturers Association 1611 North Kent Street/Suite800/Arlington, Virginia 22209])Warfield, C., Barry, M., & Anderson, L. (1986). Apparel sourcing fair addressesunemployment: Bridging retailers and the apparel industry. Journal of Home Economics, ja(4), 13-17.Wonder, J. & Donovan, P. (1984). Whole-brain thinking. New York: Ballantine Books.81Appendices82Appendix ASenior Matriculation Courses Completed by StudentsEnrolling in the Fashion Design Program at Kwantlen CollegeGrade 12 Courses CompletedSubject Art/Art Related Mathematics Home Economics1 x x2 x x x3 x x45 x x6 x x789 x x10111213 x x1415 x x1617 x x18 x x19 x x20 x x21 x x x22 x x23 x x24 x x25262728 x x29 x x30 x x x3132 x x33 x x343536833738394041424344454647xx84Note: x = course completedTotals:^30^12^32Results:^64% completed grade 12 Art26% completed grade 12 Mathematics68% completed grade 12 Home Economics81% completed grade 12 Art or Home EconomicsFrom the results of this small sample, it would appear that studentsenrolling in the Fashion Design Program at Kwantlen College aremore inclined toward the artistic than the mathematical.85Appendix BScanned Photographs of the Visual Computer Simulation Screen(Note: screen printouts were not available)Scanned photographs of the visual computer simulation screen can be found on thenext four pages. The following is a brief description of each.Figure 3 is the opening screen of the visual computer simulation. See Appendix D fora description of the objects on the screen and for a more detailed explanation of how thesimulation is used.Briefly, students use the simulation by selecting "Style" options from the spreadsheet.When the "Basic Skirt" has been selected (See Figure 4), workcentres which representeach of the steps in the production of the garment are displayed on the screen and theproduction times for each step are displayed on the spreadsheet.Students are then required to connect each of the workcentres (See Figure 5) toestablish the path that the bundles of garment pieces will follow as the garments arebeing constructed when the simulation is running.Figure 6 is an example of the style "w/PockNent" (with pockets and vent) in fullproduction. The production times for each style and answers to the first two questionsfor each style are also displayed. At this stage, students can use the simulation to makedecisions to determine which style is within the marketable price range for a hypotheticalapparel manufacturing company, by comparing production costs for the various styles.Figure 3: Opening screen for the Visual Computer SimulationFigure 4: Screen display following the selection of "Basic Skirt"111111111111111111111^OMNI^1111.111=11111111111111111 MEIMEE EMI1111111•11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111U111111111MEM NM111.111111111111111111111Figure 5: Screen display following the connection of the workcentres1114410441^,I 4^rFigure 6: Screen display of style "w/Pock/Vent in full production90Appendix CCover Letter and Consent FormFashion Design StudentsFAS 110: Development of the Apparel Industry courseKwantlen CollegeRichmond CampusOctober, 1991Dear Fashion Student:As part of my Master's thesis research, I have developed computer programs to teachapparel production design and costing. The title of my research is Effectiveness of aVisual Computer Simulation in Instruction of Apparel Production. My advisor at theUniversity of British Columbia is Dr. D. Bateson (822-5203). I would like yourassistance in field testing these computer programs.The purpose of this research study is to provide students of fashion design with arealistic learning experience relevant to production in the apparel industry. A visuallyinteractive computer simulation will be compared to a computerized spreadsheetsimulation, to teach production management to college students who tend to be moreartistic than mathematical.The study is in the context of the material normally covered in unit four: Mass ApparelProduction. The study consists of three parts. The first part is an exercise to assist inidentifying your learning style. It is not a test, for there are no right or wrong answers.The second component is an exercise to assess your ability to solve problems related todetermining production costs. Your score on this exercise will not affect your grade forthis course. The results will be used as a basis for comparison with the third componentof the study, an inclass exercise on the material covered in the simulations. The inclassexercise will count ten percent toward your final mark.As participants are being recruited from intact classes, one group will be assigned thevisual computer simulation and the other group the spreadsheet computer simulation.The entire study will take place within the normal course of events during the last threeweeks of this course. An opportunity will be provided for you to try the simulation youdid not use in the study, after the experiment is completed, should you desire to do so.All of the data collected will be confidential. You will be assigned numbers; yourname will remain anonymous. Original results for each component of the study will bereturned to you. Photocopies of the originals will be retained by me until the entire studyis completed. The photocopies will then be destroyed.If you choose not to participate in the study you will still be required to do thecomputer simulation assigned to you as well as the inclass exercise in order to completethe requirements for the course. Your final grade for this course will not be affected ifyou choose not to participate in the study.91Any participant can withdraw from the experiment at any time. Your name willremain anonymous. Again, should you withdraw from the study your final grade for thiscourse will not be affected.If you agree to participate in this study, your contribution will be greatly appreciated.Should you have any questions concerning the purpose or procedures related to thisstudy, please do not hesitate to ask. Thank you.Sincerely,Mary BoniInstructor FAS 110Office #420N Phone 599-2551Please complete and return the following Consent Form before you leave class today.CONSENT FORMTo: FAS 110 studentsFrom: Mary BoniRe: study titled Effectiveness of a Visual Computer Simulation in Instructionof Apparel ProductionI have received a copy of the cover letter and consent form that explains the purposes andprocedures of the study.Please check YES or NODo you agree to participate in this study? YES^ NO^If you said YES to the first question, do you want a copy of the study results? YES^ NO^( signature of student )92Appendix DStudent Lesson: Visual Computer Simulation (Experimental Treatment)Apparel Production ManagementA) IntroductionThe purpose of this tutorial is to provide an opportunity for you to experience howapparel factory layout designs and apparel production costs are arrived at.What you see on the screen is:a series of buttons on the left side of the screen that are to be used to runthe simulation;a sewing machine workcentre with carts on either side to display thenumber of incoming bundles of garment pieces to be sewn on the left, andthe outgoing bundles on the right;a spreadsheet that displays three skirt 'Styles' and the 'Steps' needed toproduce each style using the progressive bundling piece work system. Theaverage time to complete each step has been previously arrived at and builtinto the simulation using a probability distribution so that each time thesimulation is run, different data will be supplied. When you have finishedrunning the simulation use the bottom section of the spreadsheetto answer the seven questions related to production time and cost.- a line graph that will chart the number of garments completed per hour;- a clock that will show how much time has passed as the simulation isrunning.B) Setup and Running the SimulationYour instructor will demonstrate how the simulation works. Follow along using thishandout. Detailed instructions are provided here so that you can work independentlylater.Do the following:Select the cell that reads 'Basic Skirt' by placing the mouse cursor on the cell and clickingon the left mouse button. Wait. Observe what is now displayed on the computer screen.Now you see 5 workcentres. Each represents a step needed to construct this garmentstyle. The production time for each of the 'Steps' now appears on the spreadsheet in thecolumn below 'Basic Skirt'. In a simple factory setting, one sewing machine operator(worker) is assigned to each step.93Before you can 'Start Production' you must connect each of the workcentres to show thepath that the bundles of garment pieces will follow as the garments are being constructed.Do it now using the following directions.With the left mouse button, click on the workcentre labeled Serge Edges. A small iconin the shape of a pencil will appear on the screen. Drag the mouse straight down until thepencil and the pencil line you have drawn is in the centre of the next workcentre.Without moving the pencil, click on the left mouse button twice. The whole screen willredisplay leaving a line with an arrow displayed to show that the two workcentres arenow connected. In the centre of the second workcentre, click with the left mouse. Whenthe pencil appears, connect Darts to Zip/Seams. Then connect the rest of theworkcentres.Now you may select the button 'Start Production'. Watch the production line in operationfor 3 or 4 simulation-time hours. Note how the bundles move from workcentre toworkcentre. As a bundle moves through Finishing it is recorded on the line graph. Notehow many garments were completed per hour. When you feel that you have seenenough, select 'Stop Production' with the left mouse.C) Determining Production Times and CostsRecord the data given on the computer screen onto your copy of the spreadsheet (seepage 3). Note that the average 'Rate of Pay', in this example, for each operator, given atthe bottom of the spreadsheet, is $8.00 per hour. All of this data is to be used to answerthe questions on the following page. Write your answers to the questions on your copyof the spreadsheet.Do the questions in order, as many answers rely on the answer from the previousquestion(s).To check your answer, or for help if you are not sure how to arrive at an answer, placethe mouse cursor on the appropriate cell of the computer spreadsheet. Click the leftbutton of the mouse. You will now see a small menu that says 'help' and 'answer'. If youagain use the left mouse button to click on help, a rectangular shaped box, that providesinformation on how to go about answering the question, will pop up on the screen.Try it. Place the mouse cursor on the blank cell in the column labeled 'Basic Skirt' andthe row that refers to question 1 - '1. time/unit'. Click on the cell with the left mousebutton. Now click on 'help'. Experiment with the 'More Help' and 'Exit Help' buttonsthat you now see on the screen. When you are finished with 'help' be sure to use 'ExitHelp' to keep the screen clear.Click on the same cell again, next to question 1, but this time select 'answer' by clickingon the left mouse button.Work through the questions for the 'Basic Skirt' using the 'help' option as needed. Try toanswer each question on your own before you check the answer. Check your answers asyou go.Questions - the computer reference is in brackets following each question:1. What is the total time required to complete the constructionof one unit (garment)? See (1. time/unit) in the left handcolumn of the spreadsheet.2. How much does it cost to construct one unit? (2. cost/unit)943. How many units per hourcan each worker complete?(3. #units/hr/W)4. How many units can thisproduction setup completein one hour? (4. #units/hr.)Compare your answerto the line graph.5. How many units can thisproduction setup completein an 8 hour day?(5. #units/day)6. How much does it costto serge the edges ofeach unit? (6. cost/serge)7. If a factory needs to produce200 units per day to meet itssales quota, how manyworkcentres will be needed?(7. #workcent)STYLESSTEPS Basic Skirt w/Pockets w/Pock/VentSerge EdgesDartsPocketsVentZip/SeamsWaistbandFinishing1. time/unit2. cost/unit3. #units/hr/W4. #units/hr.5. #units/day6. cost/serge7. #workcentFigure 7: Production Times & CostsNow select the w/Pockets (with pockets) style, set up and run production as you did forthe Basic Skirt style, and work through each of the questions.Then try the w/Pock/Vent style and work through the questions without using the helpoption.95Appendix EStudent Lesson: Spreadsheet Computer Simulation (Control Treatment)Apparel Production ManagementA) IntroductionThe purpose of this tutorial is to provide an opportunity for you to experience howapparel factory layout designs and apparel production costs are arrived at.The spreadsheet you see on the computer screen displays examples of garmentproduction layouts using the progressive bundling piece work system for three skirtstyles. The 'Styles' are listed across the top of the spreadsheet. Each of the sewing'Steps' needed to construct each of the styles is listed in the left hand column, from 'SergeEdges' to 'Finishing'. The average time to complete each step has been previously arrivedat and built into the simulation using a probability distribution so that each time thesimulation is run, different data will be supplied.B) InstructionsIf you have not already selected the cell that reads 'Basic Skirt', do so now by placing themouse cursor on the cell and clicking on the left mouse button.The numbers that now appear in the column below 'Basic Skirt' are the production timesfor the 'steps' needed to construct this garment style. In a simple factory setting, onesewing machine operator (worker) is assigned to each step.Record the data given on the computer screen onto your copy of the spreadsheet that isprovided for you on the following page. Note that the 'Rate of Pay' for each operator,given at the bottom of the spreadsheet, is $8.00 per hour. All of this data is to be used toanswer the questions on the following page.The answers to the questions are to be written on your copy of the spreadsheet.Do the questions in order, as many answers rely on the answer from the previousquestion(s).To check your answer, or for help if you are not sure how to arrive at an answer, placethe mouse cursor on the appropriate cell of the computer spreadsheet. Click the leftbutton of the mouse. You will now see a small menu that says 'help' and 'answer'. If youagain use the left mouse button to click on help, a rectangular shaped box, that providesinformation on how to go about answering the question, will pop up on the screen.Try it. Place the mouse cursor on the blank cell in the column labeled 'Basic Skirt' andthe row that refers to question 1 - '1. time/unit'. Click on the cell with the left mousebutton.Now click on 'help'. Experiment with the 'More Help' and 'Exit Help' buttons that younow see on the screen. When you are finished with 'help' be sure to use 'Exit Help' tokeep the screen clear.96Click on the same cell again, next to question 1, but this time select 'answer' by clickingon the left mouse button.Work through the questions for the 'Basic Skirt' using the 'help` option as needed. Try toanswer each question on your own before you check the answer. Check your answers asyou go.C) Questions - the computer reference is in brackets following each question:1. What is the total time required to complete the constructionof one unit (garment)? See (1. time/unit) in the left handcolumn of the spreadsheet.2. How much does it cost toconstruct one unit?(2. cost/unit)3. How many units per hourcan each worker complete?(3. 4tunits/hr/W)4. How many units can thisproduction setup completein one hour? (4. #units/hr.)STYLESBasic Skirt w/Pockets w/Pocic/VentS 1 EPSSerge EdgesDartsPocketsVentZip/SeamsWaistbandFinishing1. time/unit2. cost/unit3. #units/hr/W4. #units/hr.5. #units/day6. cost/serge7. #workcentFigure 7: Production Times & Costs5. How many units can thisproduction setup completein an 8 hour day?(5. #units/day)6. How much does it cost toserge the edges of each unit?(6. cost/serge)7. If a factory needs to produce200 units per day to meet itssales quota, how manyworkcentres will be needed?(7. #workcent)Now select each of the other two styles and work through each of the questions for themas you did for the Basic Skirt.97Appendix FScanned Photographs of the Spreadsheet Computer Simulation Screen(Note: screen printouts were not available)Scanned photographs of the spreadsheet computer simulation screen can be found onthe next two pages. The following is a brief description of each.Figure 8 is the opening screen of the spreadsheet computer simulation. See AppendixE for a description of the objects on the screen and for a more detailed explanation ofhow the simulation is used.Briefly, students use the simulation by selecting "Style" options from the spreadsheet.When the "Basic Skirt" has been selected (See Figure 9), production times for each ofthe steps in the construction of the garment are displayed on the spreadsheet. The "help"option to answer question 1 is also displayed in Figure 9.SpreextitattWaistbandFigure 8: Opening screen for the Spreadsheet Computer SimulationFigure 9: Screen display using "help" to answer question 1Appendix GPretest(one mark per question, except #10 - 3 marks - Total 15 marks)No name please - just the last 4 digits of your telephone no. ^1. Divide:a..12 by .036^ b. 60 by 19.312. Convert these fractions to a decimal:a. 1/8^ b. 3.08/19.313. Multiply:a. 0.02 X 2300^ b. 14.3 X 84. Circle the largest number:2/3^4/5^3/4^5/81005. If 4 metres of fabric cost $96.00, how much will 10 metres cost?6. A stack of 40 sheets of construction paper is 2.5 cm thick.What is the thickness of one sheet of paper?7. Each of the models in the fashion show ate 2/3 of a pizza after the show.If they ate 12 pizzas in total, how many models were in the show?8. A machine sews on 225 buttons in 3 hours. There are 1000 buttons tosew on. How many will be left unsewn after an 8-hour shift?9. If it takes 20.68 minutes to sew one garment, how many garments can beproduced in 4 hours?10. Draw and label a simple illustration to show how a factory floor layoutof sewing machines, to be used to mass produce a basic skirt, would look.(3 marks)101Appendix HItem Analysis: PretestItem Analysis WorksheetTest # of Correct # of Correct DifferenceItem Responses in Responses in Between High# High-Scoring Low-Scoring & Low ScoringGroup: 27% N=14 Group: 27% N=14 Groupsla 14 4 10lb 14 10 42a 14 6 82b 13 4 93a 13 14 -13b 14 12 24 13 2 115 14 12 26 14 9 57 11 2 98 13 7 69 10 4 610 8 1 7Total: 13 itemsResults: a negative difference between the two groups of scoresoccurred in one instancea positive difference between the two groups of scores occurredfor the remaining 12 items- there was a difference of 1 to 4 between the two groups for 3of the items- there was a difference of 5 or more between the groups for theremaining 9 (69%) items102Appendix IPosttestCosting ExerciseThe last 4 digits of your phone no. ^Put your name on the back of the last page.Total marks - 15 - counts 10%Underline or circle your final answer for each question.1. Why does the designer have to cost a garment before a sample is made?(1 mark)2. a) If it takes an average time of 38.47 minutes to produce a dress and therate of pay is $7.50 per hour, what is the average cost to produce a dress?(1 mark)b) How is the average time to produce one garment arrived at? (1 mark)3. T-shirt style #402 takes an average time of 8.75 minutes to produce. Thecompany's production goal is 1000 T-shirts of that style per 8 hour day.How many machine operators are needed to meet the projected volume?(2 marks)1034. Use the following information to answer 4. a) to e).A pair of shorts takes an average of 25.13 minutes to produce and the rateof pay is $9.00 per hour:a) What is the average cost to produce one pair of shorts?(1 mark)b) If it takes an average of 5.6 minutes to sew the pockets for one unit,what is the average cost of the production of the pockets for each pair ofshorts? (1 mark)c) If it takes 2.86 minutes to serge the edges of all of the pieces for one pairof shorts, what is the average serging cost per unit? (1 mark)d) If there are 6 operators producing the shorts, how many units can thegroup complete in an 8 hour day? (1 mark)e) If the rate of pay is $8.00 per hour, what is the average cost to produceone pair of shorts? (1 mark)1045. a) If the average number of garments produced per hour per operator is2.57 and a factory needs to produce an average of 210 units in an 8 hourday to meet its sales quota, how many operators will be needed?(2 marks)b) Illustrate the production flow for a skirt with pockets using the numberof operators you determined (use your answer from 5.a) will be needed toproduce 210 units per day. (3 marks)105Appendix JItem Analysis: PosttestItem Analysis WorksheetTest # of Correct # of Correct DifferenceItem Responses in Responses in Between HighHigh-Scoring Low-Scoring & Low ScoringGroup: 27% N=14 Group: 27% N=14 Groups1 14 13 12a 14 6 82b 12 9 33 13 4 94a 14 9 54b 14 3 114c 13 5 84d 13 5 84e 14 7 75a 14 7 75b 9 3 6Total: 11 itemsResults: a positive difference between the two groups of scoresoccurred in every instance- there was a difference of 1 to 4 between the two groups for 2of the items- there was a difference of 5 or more between the groups for theremaining 9 (82%) items106Appendix KSummary of DataExperimental Group - Visual SimulationSubject PretestMax.PosttestMax. ScoreLearning StyleCategoriesThinking StyleMax. ScoreScore 15 15 a^b c d 91 4.0 5.0 0^2 -1 -2 5.72 10.0 10.0 1^-2 -1 1 5.53 11.5 15.0 0^-2 1 1 4.64 13.5 11.0 1^0 2 1 6.05 7.0 5.0 0^1 2 0 6.76 7.0 7.0 1^-4 1 1 6.27 7.5 13.0 0^2 -4 -4 4.38 6.0 7.0 0^-1 0 29 8.0 15.0 1^-4 -1 2 5.010 7.0 11.5 1^-4 0 211 8.0 8.0 0^0 -2 3 5.012 10.0 10.0 0^1 -1 1 5.913 11.0 11.0 1^4 -4 -4 5.814 9.0 12.0 1^4 -4 -4 6.115 9.0 10.0 1^-4 4 -3 4.916 14.0 9.0 1^2 -1 -1 6.417 11.0 10.0 0^4 -4 -4 5.118 6.0 11.5 1^0 -2 0 5.519 5.0 13.0 1^0 -3 1 5.520 6.0 8.5 0^4 -2 -1 5.021 12.0 13.0 1^3 -4 -1 5.822 11.0 11.0 1^-2 0 0 7.823 12.0 10.0 1^4 -4 -4 7.824 11.0 14.0 0^-1 -3 3 6.125 15.0 8.0 0^-4 0 3 5.426 4.0 5.0 0^-2 2 1 5.827 10.0 7.0 0^3 0 0 5.6Averages Interpretations & Averages9.09 10.02 a: visualization^.52 5.74b: written word .15 on a scale from60.6% 66.8% c: listening -1.1 1-9, over 5d: activity -0.2 indicates rightbrain thinker± .5 is significant107Appendix LSummary of DataControl Group - Spreadsheet SimulationSubject Pretest Posttest Learning Style Thinking StyleMax. Max. Score Categories Max. ScoreScore 15 15 a^b^c^d 91 13.0 13.0 0^3^-1^0 4.92 12.0 13.0 1^-3^0^1 4.13 14.0 13.5 1^2^-4^0 6.24 13.0 8.0 1^0^0 0 5.35 10.0 9.0 0^4^-1 -16 12.0 13.0 1^-1^0^0 5.57 11.5 14.0 -1^3^1^0 5.28 11.0 7.0 1^-4^0^1 6.59 11.0 12.0 5.010 8.0 11.0 3^-2^-4 0 6.011 13.0 12.0 1^0^-1^0 5.912 13.0 13.5 1^-4^-1^3 6.213 14.0 11.5 1^-2^-3^3 5.314 7.0 6.0 1^0^-4^2 4.515 12.0 13.0 0^4^-1^-1 6.116 10.0 12.0 1^0^0^0 5.417 12.0 12.0 -1^0^-1^3 5.918 12.0 7.5 1^3^-2 -1 4.419 11.0 14.0 1^-2^0^1 4.720 13.0 11.5 1^-4^-4^3 6.521 12.0 6.0 -1^0^0^2 5.722 11.0 9.0 1^0^-1^1 6.223 12.0 5.0 1^1^-4^124 8.0 12.0 0^0^0^2 5.225 8.0 10.0 0^-3^4 0 4.6Averages^Interpretations & Averages^11.34^10.74^a: visualization .52^5.74b: written word .15^on a scale from75.0%^69.6%^c: listening^-1.1^1-9, over 5d: activity^-0.2^indicates rightbrain thinker± .5 is significant108

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