The role of metacognitive skills in learning to solve problems

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142 ‘monitoring’. Prospective self-reported use of metacognitive skills bears no relation to learning nor do differences between conditions exist. Retrospective self-reported use of metacognitive skills shows an interaction effect (see figure 6.9). Students reporting infrequent use of metacognitive skills gain more general procedural knowledge. Figure 6.9. Overview of effects of metacognition on learning. With respect to the concurrent measurement of metacognition, a main effect of metacognition exists (see figure 6.9). Students who score high on metacognition (KM META) score higher on KM model-specific procedural knowledge than students who score low on metacognition. When the continuous concurrent variables of metacognition (G META and KM META) are used, correlational analyses show that these measures are positively related to knowledge acquisition but only in the no-model condition (see figure 6.5). In this condition, G META is positively related to the acquisition of declarative knowledge and KM META is positively related to the acquisition of KM model-specific procedural
Study III: added value of the task model 143 knowledge. Spending time in the ORGANISE phase in the no-model condition is positively related to both concurrent variables for metacognition, and spending time in this phase is also positively related to the acquisition of specific procedural knowledge. Furthermore, knowledge acquisition is positively related to transfer. This could be interpreted as indicating that the relation between metacognition and transfer is mediated by knowledge acquisition; no direct relation exists. The relation between the time spent in the ORGANISE phase, learning and metacognition could indicate the following: discussion about concepts and relations in the domain of KM stimulates the acquisition of declarative knowledge (positive relation between ORGANISE and declarative knowledge). Asking each other questions about domain knowledge stimulates the acquisition of declarative knowledge (positive relation between G META and declarative knowledge). Making contributions that indicate planning and monitoring of learning behaviour stimulates the development of intuitive task knowledge (positive relation between KM META and KM model-specific procedural knowledge). In the model condition, metacognition is not related to learning nor transfer. Spending time in the FOCUS phase is positively related to making metacognitive contributions. A possible interpretation here is that discussion in the FOCUS phase has a metacognitive component in terms of regulating learning behaviour but this does not lead to knowledge acquisition. 6.3.5.3 Construct and predictive validity of metacognition In figure 6.10 the results concerning construct and predictive validity of metacognition are displayed. The self-reported use of metacognitive skills before and after task performance are strongly related in both conditions. Concurrent measurements of metacognition are not related to the self-report measures. In the model condition, none of the measures of metacognition are related to learning results. In the model condition students make more group-related task- and metacognitive contributions than in the nomodel condition. This can be an indication that in the model condition one needs to regulate the use of the task model instead of regulating cognitive activities at the object level. In the no-model condition, the concurrent measures of metacognition are related to learning results. The amount of metacognitive domainrelated contributions positively influences the acquisition of KM modelspecific procedural knowledge. Group-related metacognitive contributions positively influence acquisition of declarative knowledge. This suggests that without a task model, regulation of the object-level results in
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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.
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
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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 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
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
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A Model-driven Approach for Buildin
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