The role of metacognitive skills in learning to solve problems

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146 inclusion of a task model specifically benefits the acquisition of taskspecific knowledge but in order to accomplish this, learners need more time and effort in order to learn how to use such a model. The results also suggest that in the end when no model is available, learners may develop ‘intuitive’ task knowledge. The retrospective self-report measure shows that metacognitive weak students gain more general procedural knowledge, regardless of condition than students who self-report to make more use of metacognitive skills. The ‘free-rider effect’(Kerr & Bruun, 1983) could explain this. Weaker students with respect to metacognition may hitchhike on the knowledge of their peers who are stronger in metacognition. The collaborative nature of the learning environment might reinforce this. Limited evidence is found for this ‘free-rider’ explanation in the mutual communication where the students scoring low on metacognition very carefully eavesdrop the students who score high on metacognition and possibly take advantage from it. With regard to the direct behavioural measures of metacognition, the correlation analysis gives some support for the interaction effect as stated in hypotheses 3 and 4. When no model is present, the use of metacognitive skills positively influences the acquisition of declarative and KM model-specific procedural knowledge. Therefore, these hypotheses are partly supported in this study. Hypothesis 7, that states a relation between transfer and the use of metacognitive skills, is not supported in this study. In conclusion, in the no-model condition, the use of metacognitive skills is related to knowledge acquisition. This is evidence that the meta-level regulates the object-level when the KM model is not available. Moreover, the results indicate that without the KM model, students still significantly acquire KM model-specific procedural knowledge. This is an indication that learners are able to develop ‘intuitive’ task knowledge. These results corroborate the theoretical model which predicts that when no task model is available, the meta-level regulates learning at the object-level and that learners may develop ‘intuitive’ task knowledge. When the KM model is included in the learning environment, students make more group-related task and metacognitive contributions in the chat than their peers who do not have the model available. Furthermore, the amount of metacognitive contributions is not related to knowledge acquisition. These results suggest that students regulate the use of the KM model, which does not influence knowledge acquisition. This represents regulation from the meta- to the task-level.
Study III: added value of the task model 147 This is some support for the theoretical model. The theoretical model predicts that the existence of a task model replaces the use of metacognitive skills in such a way that the task model regulates domain activities (from task- to object-level). The results show that the nature of the regulation changes when a task model is present. With respect to the extent to which the different measurements of metacognition adequately measure metacognition, this study supports the findings of Veenman (Veenman, in press). The three methods of measuring metacognition can be placed in a multi-trait-multi-method matrix, more specifically they are used in an across-method-and-time design. That is, different methods of measuring metacognition are used at different times. This design gives the strongest evidence for construct validity if convergence between methods occurs. With regard to the predictive validity of typical measures of metacognition for learning results, this study shows that the concurrent measurement is modestly related with learning success. Group-related metacognitive contributions explain 14% of the variance in the acquisition of declarative knowledge whereas KM-related metacognitive contributions explain 25% of the variance of the acquisition of KM model-specific procedural knowledge. This only occurs if the KM model is not present in the learning environment. The self-report measures of metacognition show no significant association with learning measures. Therefore the results in this study partly support hypothesis 5 but do not support hypothesis 7. The results of this study are in agreement with the work of Veenman (2001). He also finds that pro- and retrospective self-report questionnaires for metacognition have very low predictive value for learning, whereas measures such as protocol analysis potentially predict between 20 and 80% of the variance of learning outcomes. The conclusion is that although protocol analysis is quite labour intensive, it remains the best method for measuring metacognitive skills when one aims at predicting learning. In this thesis it is argued that one way of concurrently measuring the use of metacognitive skills is to use collaborative chat protocols. In chapter 4 it was argued that when learners are working collaboratively, they are forced to externalise their thoughts. Moreover, articulating knowledge to peer learners could induce the use of metacognitive skills. This was taken as an indication that chat protocols can give sufficient insight into the metacognitive behaviour of learners. The results of this study show that prospective and retrospective measurements do not correlate with the concurrent measure of metacognition. This lack of concordance suggests that one needs to be careful
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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
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Study I: Pilot 73 Finally, the othe
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Study I: Pilot 75 Figure 4.2. Scree
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Study I: Pilot 83 alizations of the
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86 of meaningful learning which can
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88 opportunity events (instead of t
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90 different occasions or settings.
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92 conceptual correctness (54) the
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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 134 and 135: 124 Prospective Pre-test Post-test
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 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
Page 185 and 186: Samenvatting 175 gebruik van metaco
Page 187 and 188: Samenvatting 177 nificant leereffec
Page 189 and 190: References Akhras, F. and Self, J.
Page 191 and 192: REFERENCES 181 Duffy, T. and Cunnin
Page 193 and 194: REFERENCES 183 Nelson, T. (1999). C
Page 195: REFERENCES 185 van Harmelen, F., Wi
Page 198 and 199: Prototyping of CMS Storage Manageme
Page 200 and 201: Human-Computer Interaction and Pres
Page 202: A Model-driven Approach for Buildin
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