Can CAL link the theory and practice of instruction?

CAN CAL LINK

THE THEORY AND PRACTICE INSTRUCTION?

OF

P. DAVID MITCHELL Graduate

Programme in Educational Technology, Concordia University, Montreal. Quebec,

Department of Education, Canada H3G lM8

Abstract-Typically instructional decisions reflect experience more than theory, and learning theory falls short of successful practice. Successful teaching is dynamic. It requires instructional planning skill and ability to adapt to changes in stu,dent’s capability. Good teachers monitor effects of instructional decisions on students and modify their lesson plans to improve instruction. Such feedback-controlled instructional design can link theory and practice but is a difficult skill to acquire without classroom trial-and-error. EDSIM is a CAL facility which provides practice in feedback-controlled instructional planning. It also may be used for research on instructional design. EDSIM can compress considerable teaching experience into a short period so that users can acquire the requisite flexibility in lesson planning. EDSIM simulates a class of 30 students. The users, as teacher, plans lessons which are submitted to the computer model. It updates submodels for each student and presents educational assessments after each simulated lesson. Users develop competence in instructional planning and an appreciation of CAL. Researchers can study effects of different factors.

INTRODUCTION

Most teacher trainees and some teachers have difficulty linking theory and practice. The same might be said for instructional designers. Most designers of CAL, if asked to respond to the question posed in this title probably would answer in the affirmative. CAL is based on instructional theory and incorporates: goal identification; precise specification of instructional objectives; careful and empirical design of instructional messages; diagnosis of students’ response and prescription of next lesson; perhaps probabilistic adaptive algorithms, etc. Every instructor, every instructional designer and therefore every designer of CAL lessons must take decisions about the arrangement of learning conditions, presentation of instructional communications and use of feedback from students’ responses to the lesson. In practice these instructional planning decisions reflect experience more than knowledge of the psychology of learning. Moreover not all theories are amenable to prescriptive instructional design. And the science of learning does not fully reflect what practitioners do successfully. To complicate matters many teachers do not share the instructional design perspective of CAL designers. How can the gap between theory and practice be bridged? What may facilitate the spread of CAL? This paper describes a CAL facility, EDSIM*, which can be used either for practising or for investigating instructional planning decisions. Thus it makes possible the experience and observation of the instructional design process with a view to both applying and formulating theory; equally it makes possible the implementation of instructional designs regardless of the theoretical sophistication inherent in the planner. We speculate that the practice of CAL is likely to become widespread only when teachers have had positive experiences with CAL and have accepted instructional design theory consistent with that of CAL designers. Experience with EDSIM on the part of teachers and prospective teachers may help to unite instructional theory and practice and contribute to the diffusion of CAL. THE

PROBLEM

OF

LINKING

THEORY

AND

PRACTICE

CAL and the teacher Implicit in CAL is some version of a basic instructional design model. Typically. we proceed by breaking down a course design problem into a series of lesson planning problems, each of which involves detailed planning of instructional tactics and messages within it. This decomposition method is not far removed from the premises which underly programmed instruction, though more sophisticated branching is now common. Many of the keys to successful programming of lessons are rejected or disregarded by classroom * EDSIM is available for others manual

and administrator’s

to use by post. handbook.

Researchers

295

may obtain

the programme

with a player’s

296

P.

DAVID MITCHELL

teachers. While the CAL designer has been fed a diet of convergent instructional design principles, his classroom counterpart is likely to have been nurtured in the creative or humanistic realm (with its emphasis on aesthetic rationality, search for truth, personal freedom, discovery learning, learnercentred locus of control, and transactional more than didactic lessons). Thus an imbalance of perspectives between many teachers and CAL enthusiasts seems inevitable. At the extreme view is a theory of learning which asserts that “anything that can be taught to another is relatively inconsequential . the only learning which significantly influences behaviour is self-discovered. selfappropriated learning (which) cannot be directly communicated” [ 11. Is experience the best teucher:’ Most teachers agree that they have learned their most important lessons about teaching from experience rather than from being told what to do or reading about teaching. Indeed a survey of some 140 teachers revealed only one who did not say, in response to the question, “What was the most useful part of your teacher training‘?“-practice teaching. We ignore the question of whether experience is the best teacher and consider when and why experience is such an effective teacher. One reason appears to be that when a person is in a position to choose a course of action and does so, he is forced to think about alternative courses of action that are open to him and to try to discern (and weigh the relative values of) probable consequences of each. Then he takes action and is confronted with the results. He now is in a position to analyze his choice and evaluate his decision and to consider other possible courses of action if the outcomes were not satisfactory. Real-time decision taking normally provides useful feedback whereas teacher-training seldom provides adequate experience in instructional design. Even the period of practice teaching is inadequate insofar as it forces one to design lessons that are not implemented for some time and with consequences that may be even further delayed. Real-time decisions may promote extemporaneous design and revision under pressure with little or no opportunity to study alternative courses of action. No wonder few teachers adhere to strict principles of instructional design; consider that a teacher may have 5 h to plan a l-h lesson whereas the CAL designer needs perhaps 10&-200 h to develop a satisfactory lesson. Experience teaches teachers that instructional design with attention to details (of the sort that characterizes CAL) is not viable. Some texts agree. Lack of experience as a designer of CAL is not easily corrected by teaching every teacher to prepare CAL. Nor is it likely to be quickly accomplished by making CAL lessons available as an alternative course of action (in the absence of prior experience of choosing CAL and finding it successful). Related to lack of experience is another obstacle to the teacher’s willingness to adopt CAL, the mystique that surrounds computers. Job scarcity understandably is a key concern and the situation is not helped by uncalled-for pronouncements by some enthusiasts who hinted that CAL could reduce the need for human teachers. When such extravagant notions, and growing pressure to reduce educational costs, are perceived by CAL-naive teachers, innovative work with CAL seems unlikely. We can do little about ideas popularized by others and perhaps nothing about budgetary constraints, but we can introduce teachers to CAL and make it part of their experience. We can anticipate that their initial hesitance, anxiety (and even fear) will be overcome. And we may expect some to learn more about CAL as a result. We may do so for two distinct populations: teacher trainees and practicing teachers. ADAPTIVE

INSTRUCTIONAL

DESIGN

IN

A SIMULATED

CLASSROOM

Successful teaching reflects one’s skill in instructional planning and one’s ability to note, and to consider, the effects of one’s decision on changes in students’ capability. Ability to be flexible and to respond with a wide variety of strategies is characteristic of good teachers and good CAL. As an adaptive controller the teacher monitors the effects of instruction on students and attempts to modify the content of his presentation, organization of topics, provision of opportunities for students to respond, etc. in order to improve the goal-directed process of enhancing student capability. Lesson planning and theory A lesson plan is a teaching strategy to students, procedures for shaping of relevant knowledge, selection of principles of perception, motivation,

which combines an organization of knowledge for presentation student behaviour toward intended outcomes, inducing recall instructional materials, etc. It should draw upon theoretical learning, contingency management, epistemology and measure-

Can CAL link the theory and practice or instruction?

297

ment. Further it should draw upon feedback derived from using such lesson plans to practice; this requires experience. Such feedback includes the lesson plan, students’ entering capability and changes in capability during the lesson. In short. feedback-controlled instructional design, whether for CAL or classroom instruction, requires the linking of theory and practice. But how can this be accomplished without years of study and years of experience’? Simulation for CAL

It is almost impossible to develop such instructional planning skill without the empirical revision of teaching experience yet it is unfair to students to be used as a training ground for would-be teachers. Further, many instructional design texts fail to communicate the need for feedback controlled instructional planning. How can these difficulties be overcome? How can theory be linked to practice? Simulation models can compress years of real-time experience with physical systems into a few minutes. A variant, operational gaming, permits a human game-player to participate as a decisiontaker within the structure of the system being simulated. Why not adopt this approach in order to compress years of teaching experience into a short period so that any instructor can quickly acquire the flexibility in lesson-planning and management of learning resources that normally requires years of experience’? EDSIM is such a game. It allows users to analyze practical problems of teaching in the light of their theoretical knowledge and can serve as an incentive to study relevant theory in order to play. Moreover the game administrator may use EDSIM as a research tool to discover lesson planning tactics used by different kinds of teachers. These observations may lead to further theory development. Thus EDSIM provides a two-way link to integrate theory and practice. Moreover it may be used to introduce people to the experience of interactive CAL. Teacher trainees may use it as part of their preparation whereas experienced teachers may be introduced to CAL under the guise of assisting in research into teaching decisions. It is expected that their experience with EDSIM will induce more favourable attitudes to CAL and perhaps facilitate further CAL applications if the opportunity *arises. Two versions of EDSIM exist. In one, a class of 30 students is taught simultaneously. The other permits addressing single students. EDSIM is subject-free and is applicable to most secondary and tertiary subjects.

EDSIM: A SIMULATED CLASSROOM On-line interuction between man and machine fi)r the study of instruction EDSIM provides a framework to observe and analyse instructional planning behaviour and to compress teaching experience into a short period so that teacher-trainees can acquire the requisite flexibility in lesson-planning and management of learning resources. It also is intended to force users to develop competence in the systematic planning of instruction using methods (and relevant theoretical principles) commonly used to develop CAL lessons. How does it work? Overview. EDSIM simulates a class of 30 students (each of whom is derived from an idealized model of the student as a purposeful system). The user, cast in the role of teacher, takes pre-instructional decisions to design a sequence of 50’ lessons. An overview of the model is given by Mitchell [2,3]. Decisions are selected from 26 major strategies (about e.g. objectives, teaching tactics, humour, counselling students, teaching students how to learn, etc.) (Fig. 1). The computer model calculates coefficients for each decision, updating sub-models of attention, motivation, self-management, learning and aggregate capability for each student on each of 15 curriculum topics. The computer prints educational assessments for the player qua instructional designer who then plans the next lesson using the feedback. Instructional planning for classroom teaching thus involves a cybernetic process rather than a static plan, Unlike most CAL, EDSIM does not give the player immediate feedback about whether a particular decision or sequence of decisions is deemed ‘correct’. Instead. trends emerge in rate of transition in students’ capability as a result of previous instructional strategies and decisions. The player must analyze changes in student capability and formulate hypotheses about what happened (referring to texts. discussing with others. etc.). With practice this adaptive control process improves so that the player can design successful lesson plans. By making the outcome of instruction an input to the instrllctional design process for the next period it is hoped that users will develop habits of thinking and a perspective that will generalize to regular instructional planning tasks. Input and output requmvnem The Concordia

educational

simulation

project has produced

a computer-based

game in which the

298

P. DAVID MITCHELL

1. Direct S to question, clarify or appraise the situation. Students may be slow to ask for questions or clarification. This instructional decision is intended to increase attention by requiring S to question, clarify or appraise. (time 3 min) 2. Give directions to S for reaching objectives, e.g. direct students to appropriate resources, or give the student some hints on how to tackle the problem. Student attention is assumed to be increased. (time 2 mm) 3. Discuss what the student observed or read related to the mathenlata to be learned. Here, contiguity between what is to be learned and what has been learned is assumed to be important. Discussion is rated highly as a means of facilitating the internafization of the concepts discussed. This, and other discussions, cotltr~butes greatly to the learning of the student. (time 10 min) 4. Discuss what the student read and conjectured, or hypothesized from observation. related to the mathemata to be learned. This can follow decision 3 or can be instituted without having discussed the subject previously. This decision contributes significantly to the learning process. (time IO mm) 5. Test or discuss the testing. and possible refutation, of conjectures, hypotheses or inferences generated in the above discussions. It is assumed desirable for the student to acquire the skills necessary to examine his conjectures. Such discussion and/or experimentation facilitates learning. (time 10 min) 6. State the standard of mastery expected of the student. This implies giving the student information about the response class to be expected once the learning of a stimulus-response relationship is complete. Motivation is increased. (time 5 min) 7. Solicit response from S for clarification, confirmation or correction, e.g. ask S a question, give S a test or assign homework. This is assumed to increase student attention. (time 5 min) 8. Present the mathemata to be learned. Here, using any technique, audio-visual media, etc.. the mathemata is presented to the students. Although this may involve ~nstruction~~l behaviour similar to that resulting from other decisions, this decision is regarded as subsuming a11 relevant teaching acts that have been omitted from the decision list. Overlap, to the extent that it exists, is not considered. Instructional communications implicit in this decision are extremely important for learning. (time 2 mm) the objective. 9. Ask S if he accepts or understands (In the model the student has the option ofnot accepting the assigned m~lthem~ta.) Acceptance of the m~themata, implied by discussion of a negative response as well as a positive one. increases motivation. (time 3 min) This 10. Specify stimulus class for the mathemata. involves a decision to direct s‘s attention to the object, event or attribute that represents the stimulus class in a stimulus-response relationship to be learned (whether the sttmulus is defined narrowly or broadly, e.g. a concept or principle). (time 3 mitt)

Induce recall of relevant information or behaviour. This is a decision to stimulate recognition of previously learned knowledge or skills. Continuity of related skills and knowledge is assumed to be valuable to the learning of the new mathemata. (time 5 min) of the 12. Present examples or symbolic illustrations mathemata. Similar to decision 8. (time 2 min) 13, Ask S to state his preferred objective. This decision is assumed to increase motivation (cf. decisions 23, 24) (time 2 min) 14. Response to S in order to clarify, question or appraise, e.g.: Teacher provides oral response to S; Teacher responds to S by means of a demonstration; Teacher provides written response to S. Feedback to S resulting from questions he asks, and clarification of material he receives; increases attention. (time 2 min) 15. Discuss the mathemata to be learned. This discussion is important in the instructional model. It allows the student to express himself with respect to the matbemata in question and allows the teacher to assess the extent to which the student grasps the essentials of the mathemata. (time 10 mm) 16 Introduce humour. A decision to introduce humour, whether or not it is related to the jnstru~tional objectives. wilf increase general motivation of the student slightly. (time 1 min) expected performance. The 17 Ask S to demonstrate demonstration may refer to psycho-motor, cognitive or effective domains. Knowledge that (s)he must demonstrate the expected performance is assumed to serve as motivation. (time 5 min) 18 Reinstate subordinate mathemata. Similar to decision 11except that a reinstatement of subordinate, pre-requisite or related knowledge or skills is sought. (time 5 mm) $9 Counsel S after class. If it is ever decided to discuss a student’s progress after class, it is assumed that his rate of learning with respect to the specified mathemata will increase as a result. (time I5 mm) Since 20. Help S integrate and generalize information, it may be difficult for a student to remember all the facts as individual bits of information, it is important to help him develop the skill of gene~li~in~. This dectsion is assumed to increase a student’s attention. (time 5 mm) 21 Encourage self-generated education. The learner must contintle to learn after leaving the formal education system. He will be helped if he can generate this own self-instructions, strategies and standards by which he can organize and assess his learning. This decision is important for attention (as defined by the model). (time 5 min) 22 Provide S with an opportunity to apply or use the mathemata learned for discrimination, generalization or problem-solving. Similar to decision 20, but this decision enables the student to practise making discriminations and I1

Can CAL link the theory generalizations to solve problems. This decision increases learning. (time 5 min) 23. Specify objectives for the instructional unit. Without knowing the objectives a student will progress less quickly. This decision is assumed to be of considerable importance in the learning process. (time 4 min) 24. Specify reasons for unit objectives. If a student is given a rationale for the instructional objectives, his motivation is assumed to increase. (time 3 mm)

and practice

of instruction’!

299

25. Specify instructional strategy for the unit. May be of some benefit to the student, although it is more so to the teacher. If the instructional sequence has been designed the student may be more motivated if it is presented to him. (time 3 min) 26. Teach a learning strategy. Learning will be more efficient if a student learns how to learn. The only way to be certain that a student is conversant with ideal learning skills is to teach the skills. This has a heavy weight in determining self-management and, therefore, learning rate. (lime 30 min)

Fig. 1. List of feasible instructional decisions or activities open to player

game player’s decisions stimulate instructional strategies or communications within a simulated classroom. Input. The input to our classroom simulation consists of a representation of the instructional design to be presented to the students. In real life the teacher may play many roles in fulfilling his responsibility to the student and society. Yet only a limited number of activities bring about changes in the capability of students. No attempt need be made to include extemporaneous decisions since our aim is to focus on pre-instructional decisions, i.e. lesson planning, and their probable consequence in an idealized classroom. A minimum set of alternative feasible activities open to the player must include such diverse aspects of instruction as: identifying objectives; presenting illustrations; information display; counselling students after class; soliciting responses from students; humour, etc. Instructional planning is concerned with selecting or designing instructional activities or communications to achieve a specific outcome, an increment in the student’s capability. Accordingly our input consists of a set of decisions about which instructional activities to include and how much time to allot to each. At present 26 such activities or instructional communications are included in the EDSIM Game Player’s Manual although most of these are abstractions from a wider set of possible decisions. Any instructional decision listed may be repeated as often as desired provided the total time allocated to a classroom period does not exceed 50 minutes. In EDSIM 1, each decision uses up a designated amount of time. In EDSIM 2. one may specify the time. The list of feasible instructional decisions is presented in Fig. I. The curriculum

Intended outcomes of a course-the curriculum+an be organized in a modular fashion and course planning becomes a cascaded sequence of microinstructional strategies which focus on specific objectives or units of time. Instructional planning is then concerned with selecting or designing instructional activities or communications to achieve a specific outcome, an increment in students’ capability, for each curriculum objective. In modelling possible curriculum decisions for different subjects a limitation can be turned to advantage. An artificial curriculum is required and is defined as a set of fifteen mathemata (from the Greek ‘things learned’). The player can imagine the curriculum topic or mathemata to represent a typical academic subject at the secondary or tertiary education level. The subject-free nature of this curriculum permits easy interpolation by the game-player to his own field. The EDSIM model assumes that only one mathemata can be deliberately taught at any one instant but the student’s understanding of others may improve concurrently. Several topics can be taught in a period simply by indicating a shift to a new instructional unit devoted to the new objective. (Mathemata could be, but are not currently organized into a hierarchical structure.) Output. An essential output from the model is some sort of statement about the effectiveness of the instructional planning decisions: how do they effect learning’? The instructor must have feedback to modify and refine his instructional planning decisions. The minimum information required is a statement of each student’s capability with respect to the instructional objective for a specific instructional sequence. Such a representation of a student’s capability (assessed by his performance in class) can be compared with his state prior to instruction. Then the player can arrange a set of instructional activities for the next period. Subject mastery. An underlying principle of the EDSIM model is that of mastery learning. Thus at the end of any instructional unit devoted to an objective, the student has either learned it or he has

not. If not, the probability that he wilI Iearn it in the next period may be higher. In class this is often expressed in terms of demonstrable behaviour from which the teacher infers that the subject matter has been learned. It is scarcely necessary to point out that the subtle clues useful in making such inferences are absent in EDSIM. Justified by our concern with instructional design, the factors arc too complex and elusive to incorparate into the simulated classroom, Instead, each player receives a record of his instructional decisions or communications presented to the class plus a statement of each student’s capability at the end of the period. e~p~~j~~r~ matrix. The main technique for displaying mastery is the capability matrix. This is simply a table Iisting each student’s mastery of a curriculum objective as soon as mastery can be inferred from classroom observations. usirzg EDSIM Any number of students can play EDSIM provided that adequate ~~rn~~te~ time is available and students have access either to a terminal or to a keypunch and card reader. A Game Player’s Manual is provided for each student. In addition, each student has access ta background information about EDSIM and suggested readings in learning and instructional design, (It is also possible to fabricate a simulated student’s progress record or file of relevant informatian if this is deemed useful and current research is investigating possible bias as a result of such information.) Studmts’ background Initially the user receives a class history in the form of a capability matrix. This describes the entering capability of his s~muIat~ class of 30 students with respect to each of the 15 ~urrjcuIum topics to be learned. This initial capability matrix is shown in Fig. 2. There it will be noted that each row represents a student‘s capability state with respect to 15 mathemafa. Since we assume an objective either is achieved (in which cast the cell of the matrix equals 1) or not (the cell equals O), it is apparent that no students have mastered this curriculum prior to the first class although most have mastered one or two topics. trltimately the teacher would hope that the capability vector for each student will become a sum vector as the result of his instructional strategies. In the matrix students are listed by number rather than name so that the game administrator or player can provide names freely. Given the initial capability state of his class, the player decides on such matters as which mathemata to teach, to which students% and his precise sequence of iustru~tional communjcations and strategies. How does he do this? ~~~.~~~f~f~j~~~?~~~ W&S. Foll~~~~~in~ directions in the EDSIM manuat, the player must analyze the capability matrix and decide how many i~~str~~t~onal units to pIan for the forthcoming period, each devoted to a different mathemata‘ or returning to an earlier one. (A new instructional unit also begins when a different set of students is to be taught.) It is possible to spread the simulated 50 minutes period over several topics (or in EDSIM 2, over different groups of students. e.g. where groups work at different rates), The player constructs his instructional strategy, choosing the most appropriate instructional activities from those listed in Fig. 1, Various instructional strategies can be arranged and carried out which might, in principle, alter unlearned states to learned states. The player must make his choice carefully since instructional decisions can be repeated or sequenced in different ways and some common activities are much less useful than others. Thus the player is encouraged both to use his theoretical knowledge and to reilect on the evidence avaiIab1~ to him and to consider short”term and long-term effects of teaching on his sfudents’ capability, and thereby FOadopt a learner-~ntred approach. When the entire peri#d is planned, decisions are processed by the computer program and a new ~~~~biIiry matrix is printed. In effect the EDSIM program resembfes a substitute teacher to whom the i~st~~et~r gives his lesson plan because he is unable to be present with his students in person. The substitute interacts with the students according to the instructional strategy submitted by the regular instructor and reports on the students’ capability with respect to each of the objectives dealt with in that period. Let us follow a typical instructor for a short time. Here is a sample of an instructar’s first lesson, given a class’s history. I12ifill/ instW~riolzal dt?.@ll Instru~tor~piayer A studied the available information in the initial history (cf. Fig. 2)” She decided to teach the first topic ~mathemat~~ in the first period: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 and 24. There it will be noted that the computer prints a short s~rnrn~r~

to use this set of decisions

TYPE PERIOC NC!klEER LAST COMPLETEr: TC CCNTII\JUE CR TYPE A ZERO TO START KITk: INITI.4L t:ISTOKY. ?O I!\!ITI.fiL CLASS

I:ISTCRY

CRI7'ERIOfJ ASSESSMENT-GROUP REFORT rdF?'T: IX >R(%'S=STU. WC. l-3 O< >COLS. =~~?ATIIEIMFTA NO. 1-l 5<

STUDENT NUMBERS 1 2 3 4 STU. NO. 5 c 'i E‘ 9 STU.NO.!C 11 12 1’

1; STU.

x0.

1:

16

17 31. 19 STU.1\'0.2( 21 22 23 24 STU.NC.25 25 27 2c( 29 STU.NO.3(? NE!IITOTALS

WOULD YOU ,LIKE TO PRCCEED TO PERIOD NO. l? ? yes HOW MANY UNITS ARE THERE IhJ FERIOD l? ? 1 HOW MANY INSTRUCTIONAL EECISIONS ARE IN UNIT ? 12 ENTER YOUR INSTRUCTIONAL DECISION NWEERS. ? 2 4 5 8 10 12 14 16 1F‘ 20 22 24 1 INTEMDED? FOR WHAT MATHEMATA IS UNIT ?1 Fig. 2. Initial

capability 301

matrix

of the class.

l?

PERIOI! > 1 < UbJI'i' > 1 < I'I\,THEIVlAT'A > 1 < DEC CEC DEC DEC CEC DEC DEC DEC DEC DEC DEC DEC

2 4 6 8 10 12 14 16 18 20 22 24

3IVE

DIRECTIONS FOR REACFIING OBJECTIVES. CONJECTURES FROM RELATED TOPICS. STATE THE STANDARD OF MASTERY EXPECTED.. PRESENT THE MATHEMATA TO BE LEARNED . . . . . SPECIFY STIMULUS CLASS FOR MATHEMATA.... PRESENT EXAMPLES OF TIJE MATIiEMATA....... RESPOND TO S CLARIFY-~UESTIOt~-?FFRnISC.. INTRCDUCE JIUMOUR INTO TJIE LESSON........ REINSTATE SUBORDINATE MATIIEMATA......... dELP S INTEGRATE-GENERALIZE INFCRMRTION. PROVIDE OPPORTUNITY TO APPLY MATHEMATA.. ;PECIFY REASONS FOR UNIT OBJECTIVES..... THE SUM-TOTAL OF DECISIOMS PROCESSED WAS DISCUSS

CRITERION

STUDENT NUMBERS 1 2 3 4 STU.NO. 5 6 7 e 9 STU .NO . 10 11 12 13 14 STU.b'O.15 16 17 1.9 19 STU.NO.70 21 22 23 24 STU.t\JO.35 26 27 28 2s STU.N0.20 NE% TOTR LS OLD TOTALS

PERIOD > 1< ASSESSMENT-GROUP

REFORT

not just their numbers (Fig. 3). This period would require 45 min so the class was released early. (Had it required more than 50min the students would have ‘walked out’ at that time and not all instructional decisions would be implemented.) The decisions or instr~ct~~nai communications enter the complex model and the co~es~o~di~~ change in the capability state of students is revealed by comparing the new capability matrix with the previous state. This is made easy by the placing of an asterisk above a topic column which was taught and below a column in which the end-of-period total score is greater than initial score (cf. Fig. 3). Note that initially 5 students had already mastered the first curriculum objective prior to the kzsson. After this decision 15 students now have mastered ~atb~~~ta l. With 507; of her students having mastered the objective? shoufd the instructor move on to the next ob_jective or repeat this mathemata or sort the &ass in some fashion? Many strategies, and arguments pro and con, are open. In this case the player decided to move on to plan another lesson (Fig. 4). She might have chosen to study or think about ther strategy. Further, she decided to shift to a new topic imathemata 2) whereas she could have chosen ta spend some time reviewing topic 1 before shifting to a new topic. Her instructional design appears in Fig. 4 aiong with the outcome. We can see that this lesson was a failure; only one student accptired the intended capabifity. But she did quit? No, our teacher decided to $an another lesson. She composed a lesson to review both topics (Fig. 5). Four decisions were directed to the first topic and four to Mathemata 2. We see (Fig. 6) that 25 students now have mastered the first topic and 14 have mastered the second, Now our instructional designer has decided to quit and will-we hope-bury hers& in her books on i~s~uc~oua~ theory. of the decisions,

~~~~~~~~~~&r.s& tear%&?@ We leave our player at this point. She now is free to test the effects of alternative instructional strategies and supportive action on the students in her simulated classroom. She may do so in two ways, either by planning diflcrent lessons for additional topics--in which case she runs the risk of having already induced e.g. high or low motivation in certain students or otherwise altered the composition of the cfass-or by beginning anew with a different class. The status of a class is retained if the player chooses to save the record in her class file; if not, the initial status for that session remains intact. This, too, permits one to experiment with different instructional plans. How might the player arrive at different approaches? One may resort to lengthy trial-and-error, retrieval of information from various recommended or other resources, discussion with other players or simply by thinking through the effects of diRerent Iesson plans in previous periods. The onus is placed on ihe player to find one or several ~~~~~n~~~ strategies’; this is in keeping with the model’s assumption that the learner is an adaptive, seif-organizing system which begins in a purposive state and responds diff~~entialIy to instructional communications and activities. Thus the player is forced to adopt a learner-centred approach both experientially (in her own development) and in instructional design. Does practice with EDSIM improve one’s teaching? Can EDSIM be used to collect data on how teachers plan instruction so that theoretical hypotheses may be form~Rat~d? l%e ~~~~~0~ of fhe si~~~~~~ classroom

Themajor function of EDSIhll is to serve both teacher training and research on teaching decisions by providing an instrument which includes both a theoretical and experiential framework for observa-

PERIOD > UNIT > KATIIEMATA >

? < 1 < 2<

DEC 1 DIRWT S TO QUESTION CLARIFY APPRAISE.. . CEC 2 GIVE DIRECTIONS FOR REACHING OBJECTIVES. I?EC 3 DISCUSS RELATEG OBSERVATIONS & REAfINGS. 4 DISCUSS CONJECTURES FRO&l RELATEG TCPICS. X?EC TEST OR CISCUSS TESTI!JC COt
CRITERION

STUDENT NUMBERS

STU.NO.

12 1 2 3 4 5 ;i 7 8 9

STU.NO.lG 11 12 13 14 STU.NO.15 If; 17 18 19 STU.NO.21) 21 22 23 24 STU.NO.25 26 27 28 29 STU.NO.,?O NEW TOTALS OLG TOTALS

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Fig. 4. Lesson plan and resultingcapability matrix for second period (teaching topic 1) 304

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tion and analysis of preinstructional behaviour. For example, studies are planned to compare the performance of trainees, experienced teachers and others as game-player. These should reveal strengths and weaknesses in the model as well as shedding insight into the teacher-learning process. To the extent that the simulated classroom portrays realistically the ways in which people learn, the game-player may learn useful teaching strategies. In any event it will provide practice in relevant decision-taking before going into a real classroom with its confusing demands. Whether such vicarious experience of instructional design will alter the teacher trainee’s behaviour in an ordinary classroom remains to be studied. Although this is an important part of our research, it is exceedingly difficult to validate teaching (concensus and legitimization are typically used, not empirical substantiation). Ad hoc discussions and analysis of evaluations of reports from several instructors who have worked with the EDSIM class strongly suggest that the intellectual flexibility developed here is generalizable and worthwhile though particular strategies must not be transferred wholesale. Without doubt the

306

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no Fig. 6. Capability matrix after period 3 (which was devoted to teaching both topic I and topic 2). player who successfully produces intended changes in his simulated students within the time limit has acquired many of the skills thought to be characteristic of good teachers. EDSIM also provides a new framework for research into instructional planning behaviour (e.g. how experienced instructional designers differ from novices, how a trainee changes as a result of formal teaching or study). Thus it is now possible to observe the practice of instructional planning under laboratory conditions and derive relevant principles to extend current theories of learning and instruction. In a wildly optimistic vein, perhaps EDSIM will become to instructional theory what the fruit fly has become to genetic theory! This remains problematical although preliminary work suggests that we do at least have a useful controlled laboratory which we hope to use more fully.

Can CAL Iink the theory and practice of instruction?

307

What the yame players achieve

While playing the game, the student teacher may find that adding a bit of humour tends to increase the number of mathemata learned, whereas not giving the objectives to students may decrease this number. He may first try one strategy, then another, but in every case he can obtain reports that describe the etrect of his instruction. It is in this way that a student teacher is encouraged to think about the various factors that have to be taken into account when designing instruction, and using this rational thought to design effective instruction. It must be remembered that the simulation is not meant to shape student-teachers’ behaviour in a pre-determined direction but rather to emphasize the process of taking ins~uctional decisions and of noting and reflecting on their consequences. CONCLWSlON

Instructional design practice does not always meet the minimum standards assumed by writers of texts on the subject. But even the latter rely frequently on one-shot analysis of the effectiveness of instructional products (albeit with revision and retesting). Such input-output analysis provides no insight into the cybernetics of cognition and learning. It does not even suggest what would happen if different de&i&s had been made. It is really a static model of a dynamic ~uilibrium process which we w&h to regulate. Nor does it allow the teacher or instructional designer readily to develop knowfedge of how to design instruction. More and tighter feedback loops are needed to improve ins~uctional design and instruction. EDSIM represents an attempt to conduct basic and applied research on the instructions design process. It appears to be useful not only to improve instruction directly but also to help trainees or practitioners to develop knowledge pertinent to the task of instructional design, knowledge not readily attainable without experiencing the feedback process provided. It permits data collection to promote theory development. And EDSIM users form a pool of potential CAL users. Acknowledgement-The assistance of a grant from the Quebec Minister of Education’s Programme de Formation des chercheurs et d’action concert&e, along with the assistance of several students, particularly Arthur Shears and Patrick Rose, is gratefully acknowledged. REFERENCES Rogers C. R., Freedom to Learn, A View of What ~d~cafi~~ fight Become. Columbus, Ohio: Charles Merrill (1969) Mitchell P. D., Computer Simulation of a Classroom: An Educations Game to Study me-Instructional Decisions. In Aspecrs of Edacut~o~u~Tec~no~a~~VII, (Edited by R. Budgett and J. Leedham) Pitman, London (1973). Mitchell P. D., A Simulated Qassroom and Educational Game. Adaunces in Cybctrnetics and Systems (Edited by J. Rose) Gordon & Breach, London (1974).