18
Learning and In.~uction Vol. I, pp. 3%33f1. WI Printed m Great Britain. All rights reserved. uY59-4752JYI stua+.50 0 WI Pergamon Press plc MODELLING AND COACHING OF RELEVANT METACOGNITIVE STRATEGIES FOR ENHANCING UNIVERSITY STUDENTS’ LEARNING SIMONE E. VOLET Murdoch University, Western Australia Abstract Recent developments in strategy instruction research suggest that learning in a particular discipline is enhanced by guiding students through the development of content-relevant metacognitive strategies. The potential of such an instructional approach for university settings is discussed in this paper, and is supported with evidence from an experimental field study. The study, conducted with 28 experimental and 28 matched control students over 13 weeks of an introductory computer science course, involved (a) students’ development of a metacognitive strategy relevant for computer programming, (b) modelling and coaching procedures with complete explanations about strategy use, and (c) a socially supportive learning context. The instructional method had significant short-term and long-term effects on students’ cognitive and affective learning outcomes. Introduction This paper examines how university students’ learning in a particular discipline can be enhanced by modelling and coaching metacognitive strategies that underlie expert behavior in that field. The research is based on recent theory and research on the components of effective self-regulation of learning (Brown, Bransford, Ferrara, & Campione, 1983; Paris & Byrnes, 1989; Winograd & Hare, 1989; Zimmerman, 1990). The instructional approach is inspired by Vygotsky’s (1978) theoretical assumptions of the significance of social interactions and internalization processes in cognitive development, and Brown and Palincsar’s (1989) research on reciprocal teaching, guided and co-operative learning. Empirical evidence from an experimental field study with a class of computer science students over one semester of a university course provides supportive evidence for the usefulness of the instructional approach. The use of strategy instruction as a research tool is viewed as one of the most powerful in the discipline of cognitive engineering (Belmont, 1989; McKeachie, 1988; Palincsar & Brown, 1984). Inspired by cognitive theorists’ emphases on the significance of metacognitive and volitional skills for overseeing learning (Brown et al., 1983; Corno, 1988) and by research on the significance of powerful domain-specific knowledge and Address for correspondence: S. E. Volet, School of Education, Murdoch University, Murdoch 6150, Western Australia. 319

Modelling and coaching of relevant metacognitive strategies for enhancing university students' learning

Embed Size (px)

Citation preview

Learning and In.~uction Vol. I, pp. 3%33f1. WI Printed m Great Britain. All rights reserved.

uY59-4752JYI stua+.50 0 WI Pergamon Press plc

MODELLING AND COACHING OF RELEVANT METACOGNITIVE STRATEGIES FOR ENHANCING

UNIVERSITY STUDENTS’ LEARNING

SIMONE E. VOLET

Murdoch University, Western Australia

Abstract

Recent developments in strategy instruction research suggest that learning in a particular discipline is enhanced by guiding students through the development of content-relevant metacognitive strategies. The potential of such an instructional approach for university settings is discussed in this paper, and is supported with evidence from an experimental field study. The study, conducted with 28 experimental and 28 matched control students over 13 weeks of an introductory computer science course, involved (a) students’ development of a metacognitive strategy relevant for computer programming, (b) modelling and coaching procedures with complete explanations about strategy use, and (c) a socially supportive learning context. The instructional method had significant short-term and long-term effects on students’ cognitive and affective learning outcomes.

Introduction

This paper examines how university students’ learning in a particular discipline can be enhanced by modelling and coaching metacognitive strategies that underlie expert behavior in that field. The research is based on recent theory and research on the components of effective self-regulation of learning (Brown, Bransford, Ferrara, & Campione, 1983; Paris & Byrnes, 1989; Winograd & Hare, 1989; Zimmerman, 1990). The instructional approach is inspired by Vygotsky’s (1978) theoretical assumptions of the significance of social interactions and internalization processes in cognitive development, and Brown and Palincsar’s (1989) research on reciprocal teaching, guided and co-operative learning. Empirical evidence from an experimental field study with a class of computer science students over one semester of a university course provides supportive evidence for the usefulness of the instructional approach.

The use of strategy instruction as a research tool is viewed as one of the most powerful in the discipline of cognitive engineering (Belmont, 1989; McKeachie, 1988; Palincsar & Brown, 1984). Inspired by cognitive theorists’ emphases on the significance of metacognitive and volitional skills for overseeing learning (Brown et al., 1983; Corno, 1988) and by research on the significance of powerful domain-specific knowledge and

Address for correspondence: S. E. Volet, School of Education, Murdoch University, Murdoch 6150, Western Australia.

319

320 S. E. VOLET

skills for expert performance (Chi, Glaser, & Rees, 1982; Larkin, 1982; Voss, 1986), recent strategy training studies have investigated the effectiveness of instructional packages for improving students’ learning that include a combination of both (Brown & Palincsar, 1984; Scardamalia, Bereiter, & Steinbach, 1984). The instructional focus has been on students’ development of relevant high level cognitive strategies used by competent individuals in their field of expertise. While experts often use “proceduralized knowledge” in an automatic fashion (Anderson, 1987) they have available a rich body of organized knowledge and effective cognitive skills for recognizing meaningful patterns, accessing relevant conceptual knowledge and generating appropriate problem solutions. Developmental steps towards expertise require extended practice in the application of concepts and principles to new problems (Bain, 1990). It is through repeated experience of structuring problems in accordance with first principles and then deriving solution procedures that expert knowledge can develop. One of the problems faced by instructors who are experts in their discipline relates to the fact that after years of experience, their own problem solving processes have gradually become automatized and their knowledge tacit (Johnson, 1984; Polanyi, 1973). In order to bridge the gap between their teaching objectives and their students’ competencies they need to “decompile” their own expertise (Bain, 1990), and devise ways of facilitating students’ development of expert knowledge and skills. Effective strategy instruction requires teachers to teach metacognitively (Biggs, 1987), i.e. to provide complete and explicit explanations to their students, with regards to what a new target strategy is, how, when and where to use it, as well as why it should be learnt (Winograd & Hare, 1988). Instructional methods that include a self-management component respond to the claim made by a growing number of cognitive and educational psychologists in the last decade (Brown et al., 1983; Glaser, 1984; Perkins & Salomon, 1989) that students should be taught the content of a discipline and the relevant metacognitive skills needed to handle that particular content competently at the same time. Empirical support for this “synthesis position” (Perkins & Salomon, 1989) can be found in Schoenfeld’s (1985) research in mathematics, Palincsar and Brown’s (1984) studies on reading comprehension, and Scardamelia et al. (1984) research on writing. The use of similar instructional approaches at tertiary level is not widespread, however recent research with law students is particularly promising (Lundeberg, 1987). Lundeberg identified metacognitive strategies used by legal experts and obstacles encountered by novices in reading legal cases, and investigated the effect on first year law students’ comprehension of legal cases, of being taught legal experts’ reading strategies. Inspired by the research of Brown and colleagues on strategy instruction, Lundeberg examined the significance on students’ learning, of teaching these strategies with or without self-control training and practice. The results of Lundeberg’s experimental studies clearly indicate the pedagogical usefulness of identifying the strategies used by experts in the legal discipline and of using an instructional approach that promotes explicit instruction in strategies, modelling, practice, feedback, and discussion about the use of strategies.

The growing interest among educational psychologists for instructional approaches that encourage structured forms of social interactions, collaboration and guided learning has been initiated by cognitive developmental theorists’ claim that individual thought processes have their genesis in social interactions (Vygotsky, 1978). It has been proposed and successfully demonstrated that students’ development of self-regulatory thinking skills are best acquired in interactions with others more able, when the target skills are

METACOGNITIVE INSTRUCTION AT UNIVERSITY 321

gradually transferred from an expert to a novice with guidance and coaching (Brown & Palincsar, 1989). Instructional programs where students engage in shared responsibility for thinking, expert modelling, reciprocal teaching, cognitive coaching, or other forms of guided learning facilitate their gradual internalization of more mature learners’ cognitive strategies. Giving students the opportunity to witness experts’ normally covert thinking processes, and emphasizing the gradual transfer of relevant metacognitive strategies from the teacher to the student with guidance and support are powerful cognitive tools to enhance students’ learning.

Despite considerable interest in collaborative learning in the past decade, there have been relatively few experimental studies investigating the usefulness of instructional methods promoting social interaction in university settings, and even less examining the effectiveness of using collaborative instructional methods to teach cognitive learning strategies within the context of academic disciplines. Various types of collaborative learning methods have been proposed in the literature on adult learning (Knowles, 1984; Knox, 1978; Tough, 1982) and introduced in some tertiary institutions, for example, peer tutoring and mentoring programmes, seminar discussions, group learning, team projects, and collaborative teaching and research projects (Levy-Reiner, 1985). But while such programs are described as very successful by those implementing them, little empirical evidence is provided to support their claim. In addition, since the main objective of introducing these methods was usually pragmatic, i.e. enhance teaching and learning, the conceptual and methodological issues of psychological theory and research on collaborative learning have not been addressed. When psychological principles are mentioned to support the implementation of a new program, they often appear as a convenient justification for the program rather than as its conceptual basis. While the educational potential of co-operative over individual learning has been demonstrated elsewhere (Johnson & Johnson, 1987; Slavin, 1989), there is an implicit assumption among educators of adults that by having students interact with each other, they will spontaneously develop more effective cognitive strategies and improve their learning. While it may be the case for some students, for the majority, participating in co-operative activities alone is not sufficient. Recent research on learning strategy instruction in real-life learning settings has revealed that results are dramatically improved when instructional approaches combine cognitive learning strategies with structured co-operative interactions and are embedded within the context of a discipline (Brown & Palincsar, 1984; Lambiotte et al., 1987; Lundeberg, 1987; Reeve & Brown, 1987; Scardamalia ef al., 1984). The effectiveness of this type of instruction has been measured in terms of higher academic performance, and in terms of students’ more positive self-evaluations of their learning (Dansereau, 1988).

The importance of motivational and contextual factors involved in “hot”, real-life learning is crucial and has been emphasized in recent integrative models of self-regulation of learning (McCombs, 1988; Paris & Byrnes, 1989; Schunk, 1989; Zimmerman, 1990). Self-regulated learning approaches consider learners as active participants in their own learning, emphasizing the importance of cognitive, motivational and contextual influences on students’ learning. Learners in general, and to a greater extent, adult learners are viewed as directed towards the attainment of personal goals that are determined by their cognitive and affective appraisals of the learning situation as a whole (Boekaerts, 1987; Volet & Lawrence, 1989). These cost-benefit appraisals of situations are based on students’ perceptions of the task demands, the learning situation

322 S. E. VOLET

and themselves as learners within it. Students’ appraisals and subsequent learning goals and commitment to study are crucial in academic learning as they act as mediators between students’ potential and their actual performance (Volet & Styles, in press; Volet & Chalmers, in press). Using these integrative models of learning as an interpretative framework, guided learning appears to provide a learning context particularly well suited for enhancing students’ motivation and learning satisfaction. Through guided learning, students are more likely to develop positive attitudes towards their learning because of the strong social support and because the potential power of the content-based thinking strategies that are taught is demonstrated and of immediate relevance.

The responsibility attributed to learners in these socio-cognitive-motivational theories of learning and the empirical evidence of the significance of students’ appraisal processes on their willingness to invest energy in learning tasks emphasize the need to include students’ subjective evaluations of their learning when examining the effectiveness of a new instructional approach. Despite doubts about peoples’ abilities to report on their cognitive activities (Nisbett & Wilson, 1977), most theories of adult development claim that adults are capable of self-reflection and control over their own thinking and past experiences. Individuals have privileged access to their own thoughts (Hart+ & Secord, 1973) and it is argued that they can provide sensible and valuable information on their learning, and therefore on the potential of a new instructional method. A teaching- learning approach that students themselves recognize as effective and enjoyable is likely to produce better results and is more likely to be successful than one that encounters students’ reticence, dislike or doubt about its effectiveness.

Based on theory and research on the significance of guided instruction for promoting the development of relevant cognitive skills within a particular discipline, an experimental field study was designed to investigate the usefulness of modelling and coaching relevant metacognitive strategies in a university setting, and set in a particular computer programining course. The importance of high-level thinking skills for effective computer programming has been emphasized in recent educational computing research (Dalbey & Linn, 1985; Salomon & Perkins, 1987). There is empirical evidence that expert programmers spend much of their programming time designing and planning problem solutions before coding the program into a specific language, while novices and poor performers tend to start writing computer code immediately (Dalbey & Linn, 1985; Webb, Ender, & Lewis, 1986). Linn and colleagues argue that explicit instruction in program design (essentially planning) strategies, has a beneficial effect on students’ learning, and that best results are obtained .when teachers model their own design processes at a level accessible to students (Linn, Sloane, & Clancy, 1987), or provide students with case studies of model programs with expert commentary (Linn & Clancy, 1989). But while recent studies on the metacognitive effects of learning computer programming (De Corte, Verschaffel, & Schrooten, in press; Lehrer, Guckenberg, & Lee, 1988) have revealed the significance on children’s learning of teaching relevant problem-solving skills within a programming context (Logo), there is little empirical evidence for the importance of thinking-oriented instruction in university computer science courses.

This experimental field study addressed the question of how an instructional approach involving modelling and coaching of a relevant metacognitive strategy would enhance the short-term and long-term cognitive and affective learning outcomes of university students enrolled in an introductory computer science course.

METACOGNITIVE INSTRUCTION AT UNIVERSITY 323

Method

Subjects

The experimental group (n=28) consisted of 2 intact tutorial classes (out of 9) of a first year university course in Computer Science (the programming language was Turbo Basic). The two classes were taught by the same tutor. The control group consisted of 28 matched students attending one of the other 7 tutorial classes taught by 4 other tutors. Each control student represented the best possible match for an experimental student at the beginning of the course. Based on previous research on the significance of various factors on the achievement of first year computing students pairing was based on students’ background in computing, overall program of study, gender, interest in computing and initial study goals for the computing course. Experimental and control students’ ratings of their self-competence, anticipated success, anticipated difficulty of the course and relevance of the course to their overall program of study were not significantly different.

Procedure

Recruitment of Students

The two intact classes selected to form the experimental group were chosen on the basis of timetable convenience for the tutor. In their first tutorial, experimental students were told that a new instructional method was to be introduced in their practical classes during the semester, and that based on the results of students taught in a similar way and by the same tutor the semester before, the method should be at least as effective if not better than the more traditional methods. All students agreed to be involved in the experimental tutorials.

Control students were contacted individually by the researcher, in their respective tutorial groups, half way through the course. They were told that a research project was being conducted to find out how students with different characteristics were managing their study in this introductory course. All students who were contacted agreed to be interviewed.

Instructional Approach Used With Experimental Students

The intervention with experimental students took place during tutorial time over the 13 week course (lh a week), and was conducted by the regular tutor, who was involved in the design of the study and trained in using modelling and coaching techniques. The computer programming content covered in tutorials and the time spent by tutors with their students were the same for both experimental and control groups.

The instructional package comprised: (a) a target 5-step metacognitive strategy relevant for computer programming; (b) modelling and coaching instructional techni- ques; and (c) a social support network based on collaboration and partnership.

(a) The 5-step metacognitive strategy specially designed for the intervention was essentially a planning strategy with a monitoring and evaluation component, involving:

324 S. E. VOLET

(i) problem definition; (ii) algorithm development (step by step procedure in plain English); (iii) conversion of the algorithm into the rigid formalism of a flowchart or pseudocode representation; (iv) coding from the flowchart or pseudocode into a specific programming language; and (v) execution of the code, debugging errors and improving program.

Students were required to plan their solutions by writing algorithms for their programs before entering the code into the computer. This was not normally required in the introductory computing course. However it was chosen as the target strategy because of its common use by expert programmers in real life where problems are more complex. Its potential lies in fostering students’ early development of effective skills that facilitate program development and evaluation, debugging logic errors and communication with other programmers. This strategy also has the practical advantage of being simple to understand and use, and easy to implement from the beginning, unlike other strategies such as templates (Linn & Clancy, 1989) which are more time consuming to teach.

The 5-step strategy was introduced in the first tutorial and used in all subsequent modelling exercises during the practical sessions. Students were induced to use the strategy on all their programming exercises and were given feedback on their algorithm development as well as on their completed programs. In addition to being coached how to use the strategy, students also were constantly shown the advantages of using it, whether for planning problem solutions, for trying to improve programs that already worked, or for discussing programming issues with other programmers.

(b) A major feature of the instructional approach adopted in the tutorial sessions involved an interactive teaching approach with the tutor (as the expert) with students’ gradually taking over from the tutor, modelling and coaching problem solutions, using the specially designed metacognitive strategy. Two thirds of each tutorial time was reported by students as dedicated to tutor-group verbal interactions. The programming exercises used in the modelling and coaching sessions were typical weekly exercises based on the new concepts introduced in the lectures or were past exercises presenting widespread difficulties for students. The modelling and coaching process required on-line verbalization of thought processes while processing the information, explicit and comprehensive justification at each decision point with opportunity for group discussion and sharing of alternative ideas, and continuous interfacing of theoretical concepts and principles to practical issues. Students were required to take an active part in the teaching-learning process by participating in the modelling and coaching exercises initiated by the tutor, proposing their own program solutions for examination by the group and discussing within the group the relative merits of their peers’ solutions in a critical but constructive way. Problem-solving processes that are usually covert were made public and explicit, and therefore could be examined, discussed and re-explained whenever necessary. The tutor or group provided immediate formative feedback (Striven, 1967) on the problem solutions or program improvements proposed by individuals or sub-groups.

(c) Another important characteristic of the instructional approach involved the social context of learning, made particularly supportive by use of modelling and coaching approaches and explicitly encouraging collaborative learning among students during tutorials and outside classes. From the start of the semester a system of partners (self-chosen, flexible grouping) was set up to demonstrate concretely that collaboration was a normal expectation. The intention was to introduce an intermediate step between

METACOGNITIVE INSTRUCTION AT UNIVERSITY 325

tutor and whole group’s modelling and coaching, and students’ individual work. Students were strongly encouraged to discuss their planning of problem solutions with their partners or others more expert in the group, using each other as sounding boards for alternative approaches and ideas. During consultation time, the tutor provided help to partners or sub-groups, often suggesting guidance for further discussion rather than a ready to use solution to the problem. Because discussion and collaboration were encouraged from the start, the whole class atmosphere was conducive to learning rather than performing, and students who lacked confidence in their ability at the beginning of the semester could rely on early support and guidance.

As a consequence of the modelling and coaching instructional approach, less time was left during experimental tutorials for individual consultation than was available for control students.

Instructional Approach Used With Control Students

Students in all other tutorial groups were tutored in a traditional way, i.e. they were required to work on set exercises while tutors acted as consultants with occasional group explanations. Students were not prevented from working with each other, but most of them interacted with their peers only when they needed help. Control students (like experimental students) were introduced to algorithm development in the lectures, and were encouraged to write algorithms for their programming exercises, but algorithm development was not formally required in the weekly exercises or the assignments, no strategy was taught to provide structure and guidance for planning problem solutions, and no modelling or coaching techniques were used to facilitate students’ acquisition of these skills.

Data

The evaluation of this experimental field study was monitored throughout the whole semester. Data comprised retention rates, performance marks (including four pieces of work marked by the tutors and the final examination papers marked by a single assessor blind to the experimental conditions), enrolment and achievement on the subsequent more advanced computing course, and a combination of quantitative and qualitative data on experimental and control students’ accounts and evaluation of their learning in tutorials.

All experimental and control students were interviewed in Week 5 and Week 12. In each interview, students were presented with a list of eight possible tutorial activities and asked (a) to estimate how much time, approximately and on average, was spent in each of their practical sessions on these activities, (b) to rate the usefulness of each activity for their learning progress, on a scale of 4=very to l=not at all, and (c) to indicate which of the eight activities they would have liked to see “more of” in their tutorial sessions. In the last interview only, students were required to rate (on a scale of 7=very to l=not at all) how satisfied they were in general with what they had learnt during the tutorials throughout the course, and they were asked to comment on the usefulness for their own learning of each of the three major components of the instructional package.

326 S. E. VOLET

Results

Immediate Effects of the Intervention

Retention Rates and Overall Performance

More experimental students passed the computing course compared to control students (86% vs 71%), and experimental students obtained significantly higher overall course marks than control students (71.7 vs 63.4, t45 2.18, pc.05). Six (21%) experimental students compared to only 2 (7%) control students obtained an A-grade in the course (overall mark of 80% or more). Scores on each single component of the course (two assignments, mid-semester test, end of year project, and three-part final examination) were in favor of the group of experimental students, though only the third part of the exam was statistically different. Table 1 shows the two groups’ computing performance throughout the course.

Experimental and control groups’ mean marks on the programming exercises prepared by students in their own time (two assignments a.nd major project) were not significantly different. These findings may reflect the fact that since the work was completed outside classes, students had the possibility to use any available resources to help them solve the problems (friends, textbooks, etc.), and therefore the work that was submitted for marking may not have been the best measure of students’ computer programming competence, and of their ability to solve problems independently. It should also be noted that no specific guidelines were provided for tutors to mark students’ work, which may have resulted in some tutors putting more weight on certain aspects of the programs than others.

The lack of significant difference between the two groups’ marks in the mid-semester test, deserves special attention. These tests were marked by the regular tutors, and again no specific guidelines were provided to standardize the marking. Although the question sheet did mention that the tutor would pay specific attention to the skills and techniques that were used, it is possible that some tutors based their mark essentially on their overall impression of the quality of the program without breaking it down into its components. Such an approach would tend to minimize any differences in the components or specific programming techniques that were used.

Table 1 Computing Performance of Experimental and Control Groups Throughout the Course

Experimental Control f (s.d.) f (s.d.) t

Week Assessment Weight 5 Assignment 1 10% 7.4 (1.5) 6.8 (.9) n.s. 9 Mid-Semester test 20% 14.1 (3.2) 13.2 (2.8) ns.

11 Assignment 2 10% 7.4 (1.8) 6.7 (1.1) ns. 13 Major project 20% 15.7 (2.6) 14.3 (2.8) ns. 15 Exam (part 1) 12% 7.8 (2.5) 7.7 (2.5) n.s.

Exam (part 2) 13.2% 9.0 (2.2) 8.0 (2.9) n.s. Exam (part 3) 14.8% 9.8 (2.6) 7.3 (3.5) t(45)2.74*

*p<.o1.

METACOGNITIVE INSTRUCTION AT UNIVERSITY 32-i

Students’ results in the final examination were particularly crucial for the evaluation of the intervention, because the questions that were asked permitted the researcher to investigate the type and quality of knowledge that students had acquired in the introductory course, and because the examination papers were marked by a single assessor who was blind to the experimental conditions. Both part 1 and 2 of the examination assessed students’ factual and procedural knowledge of computer programming, for example, asking for descriptions of programming concepts, functions and procedures, and reasons for using certain programming techniques. Part 3 of the exam involved a practical exercise requiring students to apply their computing knowledge to solve a fairly complex programming problem.

The two groups’ mean marks on each part of the exam revealed that experimental and control students did not differ in the amount of computer programming knowledge that they had acquired (as assessed in parts 1 and 2), but in their ability to apply their computing knowledge to solve new problems (as assessed in part 3). The highly significant difference between the two groups on part 3 of the examination (9.8 vs 7.3, t45 2.74, p<.Ol) clearly indicates that experimental students’ computing knowledge was more accessible and more usable than control students’ knowledge. Experimental students’ greater ability to apply their computing knowledge can be related to their extensive experience, through the modelling and coaching sessions, of systematically planning their problem solutions, thinking of alternative approaches and relating practical issues to programming principles.

Students’ Accounts and Evaluations of their Learning in Tutorials

In line with the design of the experimental instructional approach, experimental and control students’ estimations of the time spent on average during tutorial sessions on eight possible activities differed significantly (see Figure 1).

More time was spent overall within the experimental group, on activities involving the tutor interacting with the whole group (on average 40 min or 66.9 % of the whole tutorial time compared to 12 min or 19.9 % for control students), and consequently less time was left for the more traditional tutorial activities (independent work on the weekly exercises, getting personal help from the tutor and occasionally discussing problems with another student).

Control students reported some tutor-group activities, but significantly less time spent on each of the four tutor-group activities in both Week 5 and Week 12 (p<.OOl for each of the four activities and on both occasions). In general, control students described their tutorials as essentially set up for students to work independently on the weekly exercises, with occasional help and explanations from the tutor to individuals or to the whole group.

Despite more time available during tutorials for tutor consultation, control students reported more waiting for help (on average 3.7 min in Week 5; and 3.7 min in Week 12) than the group of experimental students (1.4 min in Week 5, t(52) 1.67, p=.ll; and 1.3 min in Week 12, t(47) -2.36, pc.05). The higher standard deviations of the control group on each of the two occasions indicate that control students who needed help were more likely to wait than experimental students in the same situation. Although these findings could be related to the reduced amount of time left in experimental tutorials for independent work, it could also indicate that since experimental students were

328 S. E. VOLET

16.2%

Total percentage of tutar-group activities: 66.9% oftuturial time

I Tutor expl. problems I Tutor theory-practice I Tutor demonstration q Group dtt altern.

[7 Working independently m Waiting for help 0 Getting tutor’s help m Working w/someone

Total percentage of tutor-group activities: 19.9% of tutorial time

53.8%

Figure 1. Percentage of tutorial time spent on average on 8 activities in experimental and control groups.

given more guidance through the modelling, coaching and discussion exercises, they were better prepared to tackle their weekly exercises, as suggested by their higher computing performance and greater satisfqction with what they had learnt during tutorials. Experimental students’ ratings of overall satisfaction with their learning during tutorial sessions, at the end of the course, was significantly higher than control students (5.8 vs 4.5, t(45) 3.70, ~K.001).

Students’ ratings (in Week 5 and Week 12) of the usefulness for their learning progress of each of the 8 activities (and regardless of how much time was actually spent on that activity) showed that experimental students found each of the four tutor-group activities significantly more useful than control students (see Table 2).

These data revealed that the quality of the tutor-group interactions was crucial, and that experimental students’ higher computing performance did not simply reflect the amount of time spent on tutor-group activities. Structured, tutor-led group learning activities involving modelling, coaching and discussions were recognised by students themselves as leading to effective learning.

METACOGNITIVE INSTRUCTION AT UNIVERSITY 329

Table 2 Experimental and Control Students’ Ratings of the Usefulness of 8 Tutorial Activities (on 2 occasions)

Week 5 Week 12

Experimental Controla Experimental Control= n=28 n=26 n=25 n=22

f (sd.) i (s.d.) t f (s.d.) 2 (s.d.) t

With whole group (1) Tutor explanation

(2)Tutor-linkingtheorytopractice

(3) Tutor demonstration

(4) Group discussion alternatives

On your own (5) Working independently (6) Waiting for help (7) Getting tutor’s help (8) Working with someone

3.5 (0.8) 2.9 (1.0) (n=24)

3.5 (0.7) 2.8 (0.9) (n=17)

3.6 (0.9) 2.7 (1.0) (n=15)

3.0 (0.8) 2.0 (1.4) (n=15)

3.5 (0.8) 3.2 (0.7)

3.5;.8) 3.5G.8) 3.2 (0.8) 3.3 (0.6)

2.34* 3.5 (0.8) 3.0 (0.9) 1.92m

3.02t 3.6 (0.8) 2.8 (1.0) 3.05t

2.85t 3.6 (0.7) 2.9 (1.1) 2.46*

n.s. 2.9 (0.6) 1.8 (1.0) 4.17$ (n=17)

n.s. 3.4 (0.6) 3.0 (0.8) n.s. - n.s. 3.4%.9) 3.6$.7) - n.s. n.s. 2.7 (0.9) 3.1 (1.0) n.s.

m = marginal p=.O6. * pc.05. t pc.01. $ p<.OOl. The number of control students who rated the usefulness of an activity is specified when some students

did not report any time spent on that activity and therefore did not rate its usefulness.

Although about two thirds of the experimental tutorial time was spent on average on tutor-group activities, still 56% of experimental students declared at the end of the course that even more time could have been spent on one or more of these activities. With regards to demonstration in particular, it was found that the percentage of students who would have liked more demonstrations increased over time for both experimental (11% to 48%) and control groups (42% to 59%). Since control students were not getting much of this type of activity in their practical sessions their request for more demonstrations was to be expected. But the increase over time in the proportion of experimental students who would have liked more demonstration revealed the success of the instructional method from students’ point of view. Students’ elicited comments on the usefulness of the approach reflected their realization that demonstrations did not aim at providing them with ready to use problem solutions, but were designed to give them a chance to see how a more competent programmer would tackle a similar type of problem so that they could learn about the strategies that were used.

He doesn’t show you the exact programs you have to write but similar ones, so you can use it and incorporate and adapt it and work through it and see how it runs and adapt that to what you need. (Exp 24, Week 12)

Students’ active involvement in the demonstration sessions combined with the requirement to make problem solving processes explicit were recognized as significant learning factors.

330 S. E. VOLET

Because you have to first explain it, explain the whole thing, so you have a lot of people working together on the same thing it makes that a lot, . . . (Exp 10, Week 5)

that’s probably where I get the most feedback on

With regard to the 5-step strategy that students had been induced to use on all their programming exercises, students’ feedback was extremely encouraging. Even students with a strong background in computing recognized the usefulness of the strategic approach, both in terms of its potential for producing effective programs and in terms of its motivational impact.

Since a large proportion of experimental tutorial time was dedicated to tutor-group activities, experimental students had less time to work on the set weekly exercises, and consequently less time available for tutor consultation on these required exercises. A crucial question was whether experimental students would indicate that they would have liked more time for personal help during tutorial sessions, since there was no other time available during the week for tutor consultation. The data showed quite the opposite. Despite reduced time for individual consultation, only 1 experimental student stated at the end of the course that he would have liked more time for personal help, compared to 8 (36%) control students x2(1) 7.96, p<.O5.

While all the structured tutor-group interactions were unanimously rated by experi- mental students as extremely useful for learning, the informal partnership system brought more varied types of responses. About half of the experimental students reported some collaborative work and rated it as very useful. High usefulness ratings came from students with high computing backgrounds as well as from students with no prior knowledge of computing, but the justification students gave for high usefulness ratings differed. Students with a good computing background found co-operation very useful to “get new ideas”, while students with little background thought that it was useful to help them tackle the more difficult programming exercises. The other half of the experimental group did not rate collaboration with someone else as very useful for their learning, but students specified that their rating was related to the fact that they were not spending much time on this kind of activity. Among these students, those with a good or average background in computing declared that they could not see any need for one to one collaboration, because most of the time both partners already knew how to tackle the required set exercises. The few students with no background in computing who reported minimum co-operation with other students and consequently gave a low usefulness rating to this activity, simply said that they did not make the effort to work with others.

Long-term Effects of the Intervention

More experimental than control students passed the follow-up more advanced course in computer science the semester following the intervention. In addition, the group of experimental students who completed that course performed significantly better than the group of control students. Figure 2 illustrates the computing achievement of experimental and control students in the Introductory course and in the Advanced course one semester later.

As can be seen in Figure 2, 18 (64%) of the initial 28 experimental students enrolled in the advanced computing course the semester after the intervention, compared to only

METACOGNITIVE INSTRUCTION AT UNIVERSITY 331

lm; %- experimental

80 86% 71.7 (13.2) ilmmomm control

p

71% 63.4 (12.7)

\6@w 61% 70.8 (11.6)

s a- * 439bouluHllp 36% 60.9 (13.6)

20-

Introductory course _t Advancedcourse

enrolled passed mark (s.d.J enrolled passed mark (s.d.)

Figure 2. Computing achievement of experimental and control groups in the introduction course, and the Advanced course one semester later

12 (43%) of the initial 28 control students. While enrolment figures for the two groups were not statistically different, the percentage of experimental students who passed the course was significantly greater than the percentage of control students (61% vs 36%, x*(l) 3.5, p-C.05, one-tail test).

The long-term positive effect of the intervention on students’ computing competence was revealed by comparing the performance in the follow-up advanced computing course of the 18 experimental and the 12 control students who had completed that course (pass and fail students included). The mean overall course mark of the group of experimental students’ was significantly higher than the mean mark of the group of control students (70.8 vs 60.9, t(28) 2.07, pC.05).

In summary, the results showed the positive immediate and long-term effects of the instructional approach on students’ computing performance, on their personal satisfaction with the learning experience and on their motivation for further study in computing.

Discussion

The experimental study demonstrated the significance on students’ cognitive and affective learning outcomes of modelling and coaching a content-relevant metacognitive strategy within the context of an academic course. The study was successful in fostering students’ development of effective skills for solving computer programming problems and contributed to their sound understanding of computer programming principles. Students’ performance on the more challenging question of the examination revealed that their knowledge was not limited to the acquisition of syntactic knowledge of language features and algorithmic skills for solving typical programming exercises. Students had developed a rich body of organized, abstract programming knowledge and strategic techniques to tackle novel problems and design effective solutions. The significance of fostering the early development of a strong conceptual and strategic knowledge base was reflected in students’ superior performance not only in the course under study but also in the more advanced course.

The study supported cognitive and educational psychologists’ claims that metacognitive

332 S. E. VOLET

strategies should be taught not as abstract, domain-independent heuristics but as discipline-relevant strategies (Glaser, 1984; Perkins & Salomon, 1989). The results show how impressive gains can be obtained when a relevant metacognitive strategy is taught in the context of a particular discipline. The strategy selected for the intervention was instrumental in fostering students’ reflection and planning of their programming problem solutions. Perkins and Salomon (1989) argue that general cognitive skills act as cognitive tools or “gripping devices” for handling and managing the discipline knowledge in question. In this study, the 5step metacognitive strategy not only induced students to plan their problem solutions, but provided a structure to guide this planning process. Students were taught not only how to use the strategy but were given complete explanations and demonstrations about when, where and why the strategy was useful (Winograd & Hare, 1988), and were therefore induced to reflect on their problem solving processes and to make informed decisions regarding the use of their computing knowledge. The reflective-fostering nature of this instructional approach is similar to Salomon and Globerson’s (1987) notion of “mindful” learning. Salomon and Globerson argue that instigating mindfulness during the process of instruction produces better comprehension, and leads to the development of principled knowledge that has been decontextualized from its initial context and therefore is available for transfer by abstraction. The fact that experimental and control students did not differ at the end of the course with regards to their knowledge of computing techniques (questions 1 and 2 of the examination), but differed in their ability to use these techniques on novel problems (question 3) supports this assumption. Experimental students had not developed a greater amount of computing knowledge, but the knowledge that they had acquired was flexible and accessible for transfer (Brown & Campione, 1981), because it was the result of mindful reflection, consideration of alternatives, logical deduction and comprehension. Increasing mindfulness during instruction through metacognitive teaching is an extremely useful instructional approach for helping novices to go beyond the acquisition of automatic but limited application of factual and algorithmic knowledge, and to facilitate their gradual development of the rich knowledge base that underlies expert performance.

While the significance of teaching high-level thinking strategies embedded within the context of a discipline has been acknowledged by a growing number of educational psychologists, the next important issue on the agenda is to investigate how such programs can be best implemented in real-life learning settings where motivational and contextual factors interact with cognitive issues. An important feature of successful strategy training is to convince students that the target strategy is useful and relevant, and will lead to improvement of their performance, especially if using the target strategy requires taking the “high road” of learning (Salomon & Globerson, 1987), a more mentally demanding route to the acquisition of knowledge and skill, as was the case in this study. This issue is particularly crucial with adult students because of their tendency to weight the relative cost and benefits of making additional effort, and their lack of willingness to invest energy in a learning task if it is perceived as irrelevant, boring, too difficult or trivial (Boekaerts, 1987; Volet & Styles, in press). In this study students had a chance to experience, realize and discuss the usefulness and relevance of the target strategy. Even students with prior experience of programming adopted a positive attitude about the planning strategy, and despite some early perceptions that the small exercises could easily be solved without much planning, all students eventually expressed a belief that

METACOGNITIVE INSTRUCTION AT UNIVERSITY 333

learning to use the strategic approach was worth the extra time and effort (Palmer & Goetz, 1988).

Students’ commitment, internalization and successful use of the target strategy on novel problems was due in large part to the social nature of the instructional model adopted for the intervention. The study strongly supported previous research on the significance of modelling, coaching and guided learning techniques for promoting the development and efficient use of metacognitive strategies. By participating in the tutor-group learning session, students were gradually introduced to expert computer programmers’ strategies for tackling problems. The social context of learning provided the rationale for inducing students to provide justifications and explanations for their strategy use. The demonstrations, involving detailed explanations of reasons for problem-solving decisions with regard to computing principles and where appropriate, discussion and comparisons of alternative problem solutions, facilitated students’ development of informed knowledge of computer programming skills. A crucial feature of the instructional approach was students’ involvement in the modelling and coaching sessions. Because the tutor-group activities were explicitly set up to promote students’ learning rather than competition and evaluation, students were encouraged to participate by proposing their own problem solutions for discussion, critically evaluating each other’s programs and collaborating in the group’s effort to to find out how programs that already worked could be improved. These structured forms of social interactions facilitated students’ development of initial competence with guidance, and provided an effective intermediate step between expert demonstrations and students’ independent work.

The socially supportive nature of the learning context also contributed to the success of the study. Students’ elicited comments on the practical sessions reflected their satisfaction with the positive learning atmosphere of the tutorial sessions, and were in line with the higher retention rate within the group of experimental students, both overall and within the sub-group of students without a computing background. These findings agree with Johnson, Johnson, and Stanne’s (1986) findings that computer-assisted co-operative learning produce higher performance than individualistic or competitive learning settings. The importance of adult students’ perceptions and subjective evaluation of their learning in an academic course should not be minimized, as they have been found to be determinant factors in students’ decisions to stay in that course (Volet & Styles, in press). With regards to strategy instruction, it has been claimed that the effectiveness of an intervention program requires the commitment, enthusiasm and competence of the instructor (Palincsar & Brown, 1984; Linn & Clancy, 1989; McKeachie, 1978; Miles, 1988). This issue is particularly crucial if the implementation of a strategy instruction program is planned in university settings, where lecturers are experts in their discipline but are often untrained teachers. When adult learners are targeted, success of the intervention is not only affected by the quality of the teaching, but also by students’ cognitive and affective appraisals of the learning situation. McCombs (1988) cogently argues that strategy training programs should include a motivational component in order to “fuse cognitive skill and motivational will” (Paris, 1988a). The importance of students’ personal satisfaction with a learning experience not only sustains their motivation to invest energy in the task, but leads to motivation for further learning, as reflected in this study. Few experimental strategy instruction studies have included in their measures of success the affective outcomes of students’ learning (Spurlin, Dansereau, Larson, &

334 S. E. VOLET

Brooks, 1984). If the results of laboratory experiments are to be successfully implemented in real-life learning settings, students’ subjective evaluations of new instructional methods will need to be sought and taken into account.

While it is claimed that the impressive and durable findings of the intervention were related to a combination of content-specific relevant strategy instruction, modelling and coaching instructional approaches and a socially supportive learning context, it is possible that only one or two components of the instructional package were primarily responsible for students’ higher performance. The fact that the intervention was conceptualized as an instructional package makes it impossible to dissociate the effect of teaching a relevant metacognitive strategy, from the modelling and coaching approach, or from the social support provided to students through the structured tutor-group activities. Since the ultimate aim of this study was to obtain a significant improvement in students’ computing performance, this issue was not addressed in this study. However, it is now widely recognized that the most successful intervention programs combine a number of characteristics which address the cognitive, motivational and contextual issues involved in learning. Paris (1988b) has summarized a number of axioms of successful strategy training which range from the meaningfulness and effectiveness of the target strategies; the explanations provided about the relevance, potential and application of the strategies across tasks; the effort made to enhance students’ motivation to use the strategies; and the instructional method adopted to foster students’ development and independent use of the strategies. All these issues were addressed in this study and it is assumed that they contributed jointly to the success of the experiment. As suggested by Palincsar and Brown (1984), since the cognitive outcomes of strategy instruction studies are still limited, particularly in the area of adult learning, it is advisable to attempt first to “obtain a sizable, durable and generalized effect”, and then try to establish the respective contribution of each sub-component of a new instructional approach.

In summary, a case is made for the significance of promoting university students’ development of metacognitive strategies within the contextual requirements of a specific discipline, and for using instructional approaches that foster social interactions, in particular that promote the gradual transfer of relevant high level thinking skills, with guidance and coaching, from an expert (instructor) to a novice (student). The major limitations of this study relate to the relatively small sample size and the presence of only one experimental tutor, which suggest some caution in generalizing from the results. While the matched pair design allowed to control variables on the side of the subjects, the “single case” tutor made it impossible to dissociate the effect of the instructional method from a possible tutor effect. But despite some limitations, this experimental field study with carefully matched controls, an ecologically valid learning setting which involved learning over the natural time frame of a course of study, multiple criteria for success including cognitive and affective learning outcomes, and short-term and long-term effects of the intervention, provided a strong support for the educational potential of the instructional approach for enhancing university students’ learning.

References

Anderson, J. R. (1987). Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 94, 192-210.

Bain, J. D. (1990). Unpacking the expert to teach the novice. In M. Kratzing (Ed.), Proceedings of the 8th Australasian learning and language conference (pp. 11%127). Brisbane, Australia: QUT Press.

METACOGNITIVE INSTRUCTION AT UNIVERSITY 335

Belmont, J. M. (1989). Cognitive strategies and strategic learning. American Psycholog&, 44, 142-148. Biggs, J. B. (1987). Student approaches to learning and studying. Hawthorn, Vie.: ACER. Boekaerts, M. (1987). Individual differences in the appraisal of learning tasks: An integrative view on

emotion and cognition. Communication and Cognition, 20, 207-224. Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering and

understanding. In J. H. FIavell & E. M. Markman (Eds.), Carmichael’s mnnual of child psychology: Vol.1. Cognitive development (pp. 77-166). New York: Wiley.

Brown, A. L., & Campione, J. C. (1981). Inducing flexible thinking: A problem of access. In M. Friedman, J. P. Das, & N. O’Connor (Eds.), Intelligence and learning (pp. 515-529). New York: Plenum.

Brown, A. L., & Palincsar, A. S. (1989). Guided cooperative learning and individual knowledge acquisition. In L. B. Resnick (Ed.), Knowing, learning and instruction: Essays in honor of Robert Closer (pp. 393-451). Hillsdale, NJ: Erlbaum.

Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem-solving. In R. Stemberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7-75). Hillsdale, NJ: Erlbaum.

Corno, L. (1988). Self-regulated learning: A volitional analysis. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement (pp. 111-142). New York: Springer.

Dalbey, J., & Linn, M. C. (1985). The demands and requirements of computer programming. A review of the literature. Journal of Educational Computing Research, 1, 253-274.

Dansereau, D. F. (1988). Cooperative learning strategies. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction and evaluation (pp. 103-120). San Diego: Academic Press.

De Corte, E., Verschaffel, L., & Schrooten, H. (in press). Cognitive effects of learning to program in Logo: A one-year study with sixth graders. In E. De Corte, M. Linn, H. Mandl, & L. Verschaffel (Eds.), Computer-based learning environments and problem-solving. Berlin: Springer.

Glaser, R. (1984). Education and thinking: The role of knowledge. American Psychologist, 39, 93-104. HarrC, R., & Secord, P. F. (1973). The explanation of social behavior. Totowa, NJ: Littlefield &

Adams. Jagacinski, C., Lebold, W. K., & SaIvendy, G. (1988). Gender differences in persistence in computer-related

fields. Journal of Educational Computing Research, 4, 185-202. Johnson, D. W., & Johnson, R. T. (1987). Learning together and alone: Cooperative, competitive and

individualistic learning. London: Prentice-Hall. Johnson, P. E. (1984). The expert mind: A new challenge for the information scientist. In T. M. A.

Bemelmans (Ed.), Beyond productivity: Information systems development for organizational effectiveness (DD. 367-386) Amsterdam: North Holland.

Johnson, R. T:, Johnson, D. W., & Stanne, M. B. (1986). Comparison of computer-assisted cooperative, comoetitive. and individualistic leamina. American Educational Research Journal, 23, 382-392.

Know&s, M. S. (1984). Andragogy in act?on. San Francisco: Jossey Bass. Knox, A. B. (1978). Adult development and learning. San Francisco: Jossey Bass. Lambiotte, J. G., Dansereau, D. F., Rocklin, T. R., Fletcher, B., Hythecker, V. I., Larson, C. O.,

& O’Donnell, A. M. (1987). Cooperative learning and test taking: Transfer of skills. Contemporary Educational Psychology, 12, 52-61.

Larkin, J. H. (1982). The role of problem representation in physics. In D. Gentner & A. Collins (Eds.), Mental models (pp. 75-98). Hillsdale, NJ: Erlbaum.

Lehrer, R., Guckenberg, T., & Lee, 0. (1988). Comparative study of the cognitive consequences of inquiry-based Logo instruction. Journal of Educational Psychology, 88, 543-553.

Levy-Reiner, S. (Ed.) (1985). Collaborative learning. Forum for Liberal Education, 8, 2-18. Linn, M. C., SC Clancy, M. J. (1989, March). The case for case studies of programming problems. Paper

presented at the annual meeting of the American Educational Research Association, San Francisco, California.

Linn, M. C., Sloane, K., & Clancy, M. (1987). Ideal and actual outcomes from precollege Pascal instruction. Journal of Research in Science Teaching, 24, 467-490.

Lundeberg, M. A. (1987). Metacognitive aspects of reading comprehension: Studying understanding in legal case analysis. Reading Research Quarterly, 22, 407-432.

McCombs, B. L. (1988). Motivational skills training: Combining metacognitive, cognitive and affective learning strategies. In C. E. Weinstein, P. A. Alexander, & E. T. Goetz (Eds.), Learning and study strategies: Issu& in assessment instruction and evaluation (pp. 141-170). New York: Academic-Press.

McKeachie. W. J. (1978). Teachina tips. Lexington, MA: D.C. Heath. Miles, C. (1988). Cognitive strategies: Implica?ions for college practice. In C. E. Weinstein, E. T. Goetz,

& P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction and evaluation (pp. 333-347). San Diego: Academic Press.

Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231-259.

336 S. E. VOLET

Palincsar, A. S., & Brown, A. (1984). Reciprocal teaching of comprehension - fostering and comprehension - monitoring activities. Cognition and Instruction, 1, 117-175.

Palmer, D. J., & Goetz, E. T. (1988). Selection and use. of study strategies: The role of the studier’s beliefs about self and strategies. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and studv strategies: Issues in &sessment, instruction and evaluation (pp. 41-61). San Diego: Academic P&s.

Paris, S. G. (1988a). Fusing skill and will in children’s learning and schooling. Paper presented at the American Educational Research Association, New Orleans.

Paris, S. G. (1988b). Models and metaphors of learning strategies. In C. E. Weinstein, P. A. Alexander, & E. T. Goetz (Eds.) Learning and study strategies: Issues in assessment instruction and evaluation (pp. 299-321). New York: Academic Press

Paris, S. G., & Byrnes, J. P. (1989). The constructivist approach to self-regulation of learning in the classroom. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement (pp. 169-200). New York: Springer.

Perkins, D. N., SC Salomon, G. (1989). Are cognitive skills context-bound? Educational Researcher, 18 (1) 16-25.

Polanyi, M. (1973). The tacit dimension. Garden City, NY: Doubleway. Reeve, R. A., & Brown, A. L. (1985). Metacognition reconsidered: Imulications for intervention research.

Journal of Abnormal Psychology, lj, 343-356: Salomon. G.. & Globerson. T. (1987). Skill mav not be enough: The role of mindfulness in learnine and

transfer. International Journal of Educational Research, 11, 623-637. .z

Salomon, G., & Perkins, D. N. (1987). Transfer of cognitive skills from programming: When and how? Journal of Educational Computing Research, 3, 149-170.

Scardamalia, M., Bereiter, C., & Steinbach, R. (1984). Teachability of reflective processes in written composition. Cognitive Science, 8, 173-190.

Schoenfeld, A. H. (1985). Mathematicul problem solving. Orlando, FL: Academic Press. Schunk, D. H. (1989). Social cognitive theory and self-regulated learning. In B. J. Zimmerman & D. H.

Schunk (Eds.), Self-regulated learning and academic achievement (pp. 83-110). New York: Springer. Striven, M. (1967). The methodology of evaluation. In R. W. Tyler, R. M. Gag&, & M. Striven (Eds.),

Perspectives of curriculum evaluation. Chicago: Rand McNally. Slavin, R. E. (1989). Cooperative learning methods. In R. E. Slavin (Ed.), School and classroom organisation

(pp.131-135). Hillsdale, NJ:Erlbaum. Spurlin, J. E., Dansereau, D. F., Larson, C. D., & Brooks, L. W. (1984). Cooperative learning strategies

in processing descriptive text: Effects of role and activity level of the learner. Cognition and Instruction, 1, 451-463.

Tough, A. (1982). International changes. Chicago: Follett. Volet, S. E., & Chalmers, D. (in press). Investigation of qualitative differences in university students’ learning

goals based on an unfolding model of stage development. British Journnl of Educational Psychology. Volet, S. E., & Lawrence, J. A. (1989). Goals in the adaptive learning of university students. In H. Mandl,

E. De Corte, N. Bennett, & H. F. Friedrich (Eds.), Leurning and instruction: European research in an internutional context (Vol. 2-3, pp. 497-516). Oxford: Pergamon.

Volet, S. E. & Styles, I. (in press). Predictions of study management and performance on a first-year computer course: The significance of students’ study goals and perceptions. Journal of Educational Computing Research.

Voss, J. F. (1986). Social studies. In R. F. Dillon & R. J. Sternberg (Eds.), Cognition and instruction. (pp. 205-239). Orlando: Academic Press.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman Eds. ‘& Trans.). Cambridge, MA: Harvard University Press.

Webb, N. M., Ender, P., & Lewis, S. (1986). Problem-solving strategies and group processes in small group learning computer programming. American Educational Research Journal, 23, 243-261.

Winograd, P., & Chou Hare, V. (1988). Direct instruction of reading comprehension strategies: The nature of teacher explanation. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction and evaluation (pp. 121-139). San Diego: ‘Academic-Press. .

Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist. 25, 3-17.