The role of metacognitive skills in learning to solve problems

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124 Prospective Pre-test Post-test Declarative MS− .50 .11 24 .62 .11 24 MS+ .52 .12 19 .64 .10 19 Specific procedural MS− .45 .17 24 .63 .14 24 MS+ .53 .15 19 .66 .13 19 General procedural MS− .48 .14 24 .60 .10 24 MS+ .54 .12 19 .58 .11 19 Transfer MS− .70 .07 24 MS+ .70 .09 19 Table 6.9. Mean, standard deviation and number of participants for the pre- and post-test scores on declarative and general procedural knowledge and transfer measured for high and low scorers on metacognition (prospectively measured) in proportion of correct answers. Retrospective Pre-test Post-test Declarative MS− .50 .12 24 .63 .12 24 MS+ .51 .10 22 .61 .09 22 Specific procedural MS− .46 .18 24 .63 .15 24 MS+ .51 .14 22 .63 .12 22 General procedural MS− .49 .12 24 .63 .09 24 MS+ .54 .13 22 .55 .09 22 Transfer MS− .71 .07 24 MS+ .69 .09 22 Table 6.10. Mean, standard deviation and number of participants for the pre- and post-test scores on declarative and general procedural knowledge and transfer for high and low scorers on metacognition (retrospectively measured) measured in proportion of correct answers.
Study III: added value of the task model 125 As far as interaction effects are concerned, only one such effect exists, a within-subjects interaction effect between acquiring general procedural knowledge (from pre- to post-test) and metacognition measured retrospectively (F = 4.55, p < 0.05). Students who score low on the use of metacognitive skills measured after task performance show more learning with respect to general procedural knowledge than students who score high on the use of metacognitive skills. It appears that weaker students in terms of self-reported use of metacognitive skills after task performance learn most, regardless of condition. A possible explanation for the interaction effect found could be that students in the MS+ group already perform better on the pre-test than students in the MS− group because they have a relatively good framework of general metacognitive skills available that helps them in answering more questions correctly after they have had the introductory training session. A side-effect then could be that this overshadows a potential learning effect. The a priori comparisons show, however, that no differences exist on pre-test scores. Therefore this explanation is not corroborated. An alternative exploratory perspective on this interaction effect is the following: perhaps students who report the less frequent use of metacognitive skills pay more attention to what the students say who report the more frequent use of these skills in the chat facility. The former students could learn a lot from the latter, without actually having to put in the effort. This effect is known in the literature as the ‘free-rider’ or ‘social loafing’ effect (Kerr & Bruun, 1983). The free-rider effect in collaborative learning situations concerns team members who avoid expending effort on the task because the other team members put in enough effort. A quantitative investigation of whether the free-rider effect occurs, is performed. The distribution of high and low scores on metacognition within teams is explored. Table 6.11 shows the distribution. This effect could be expected to occur particularly in mixed teams (in which team members differ in self-reported metacognitive skills). This concerns 12 out of 16 teams. The question is whether the members of these 12 teams perform differently from the members of the remaining four teams. A t- test reveals that no significant differences exist on the post-test of general procedural knowledge. In order to pursue the exploration in a qualitative way, the communication protocols are investigated. If this idea were to be true then one should find that students who report less frequent use of metacognitive skills (and gain high scores on general procedural knowledge), are rather passive in their communication whereas the students who report the
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The role of metacognitive skills in
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The role of metacognitive skills in
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Contents 1. INTRODUCTION 1 2. THEOR
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Contents ix 6. STUDY III: ADDED VAL
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xii Rienke Schutte, zonder de medew
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2 in realistic settings and everyda
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4 use a trade-off between efforts i
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6 of this thesis are described. In
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8 First an overview of relevant lit
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10 In research concerning reading a
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12 problem has actually been found
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14 or whether domain specific metac
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16 learners should be able to artic
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18 piece of origami paper. Both mat
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20 tion given to the problem solver
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22 is rather directive. It does not
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24 The problem solver has declarati
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26 problem and the fact that studen
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28 learn from them. In terms of Jan
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30 This model is called DORMOBILE (
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32 Second, the meta-level in proble
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34 Figure 2.8. Theoretical model of
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36 task-level. In order to keep the
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Chapter 3 KM QUEST The goal of this
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KM Quest 41 Figure 3.1. The Knowled
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KM Quest 43 the fact that some plan
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KM Quest 45 a dynamic simulation-ga
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KM Quest 47 Quest. They are visuali
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KM Quest 49 cesses in Coltec. For e
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KM Quest 51 Figure 3.5. The Knowled
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KM Quest 53 3.3.1.3 The IMPLEMENT p
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KM Quest 55 process in an electroni
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Chapter 4 STUDY I: PILOT This chapt
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Study I: Pilot 59 the spontaneous o
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Study I: Pilot 61 Also, the questio
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Study I: Pilot 63 however, to what
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Study I: Pilot 65 ceived 12 questio
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Study I: Pilot 67 objective, a seco
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Study I: Pilot 69 other. It also ex
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Study I: Pilot 71 In conclusion, th
Page 83 and 84: Study I: Pilot 73 Finally, the othe
Page 85 and 86: Study I: Pilot 75 Figure 4.2. Scree
Page 93: Study I: Pilot 83 alizations of the
Page 96 and 97: 86 of meaningful learning which can
Page 98 and 99: 88 opportunity events (instead of t
Page 100 and 101: 90 different occasions or settings.
Page 102 and 103: 92 conceptual correctness (54) the
Page 104 and 105: 94 The score on KMQUESTions indicat
Page 106 and 107: 96 is a variable that influences th
Page 108 and 109: 98 5.4 Discussion The hypothesis fo
Page 110 and 111: 100 mal company results. In the fol
Page 112 and 113: 102 more inclined to regulate domai
Page 114 and 115: 104 these skills in order to solve
Page 116 and 117: 106 Hypothesis 1: students acquire
Page 118 and 119: 108 KMQUESTions of study II was per
Page 120 and 121: 110 The second measurement of game
Page 122 and 123: 112 T-PROS because they did not occ
Page 124 and 125: 114 Category Rule Example Task Indi
Page 126 and 127: 116 vised by one experimenter in or
Page 128 and 129: 118 6.3 Results 6.3.1 Game results
Page 130 and 131: 120 to Coltec. Only threats have a
Page 132 and 133: 122 6.3.2.1 Effects of learning and
Page 136 and 137: 126 Distribution Frequency 1 high 2
Page 138 and 139: 128 No − model Model Retrospectiv
Page 140 and 141: 130 model condition. Perhaps, perfo
Page 142 and 143: 132 Cog Tas Meta Total No-model con
Page 144 and 145: 134 Condition * Metacognition G MET
Page 146 and 147: 136 Figure 6.5. Relations between m
Page 148 and 149: 138 in the model condition: Visuali
Page 150 and 151: 140 Figure 6.7. Overview of relatio
Page 152 and 153: 142 ‘monitoring’. Prospective s
Page 154 and 155: 144 learning. Finally, in the no-mo
Page 156 and 157: 146 inclusion of a task model speci
Page 158 and 159: 148 interpreting the results on met
Page 160 and 161: 150 When no task model is available
Page 162 and 163: 152 Another perhaps more valid expl
Page 164 and 165: 154 havioural measures prove to be
Page 167 and 168: Appendix A Solving a problem in KM
Page 169 and 170: APPENDIX A: Solving a problem in KM
Page 171: APPENDIX A: Solving a problem in KM
Page 174 and 175: 164 to learning to solve problems.
Page 176 and 177: 166 cognition in a constructivist l
Page 178 and 179: 168 forward. The most important one
Page 181 and 182: Chapter 9 SAMENVATTING Het huidige
Page 183 and 184: Samenvatting 173 De aanwezigheid va
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Samenvatting 175 gebruik van metaco
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Samenvatting 177 nificant leereffec
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References Akhras, F. and Self, J.
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REFERENCES 181 Duffy, T. and Cunnin
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REFERENCES 183 Nelson, T. (1999). C
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REFERENCES 185 van Harmelen, F., Wi
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Prototyping of CMS Storage Manageme
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Human-Computer Interaction and Pres
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A Model-driven Approach for Buildin
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