The role of metacognitive skills in learning to solve problems

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$Learning LATEX by Doing$

178 en transfer ondersteunen. De verklaring hiervoor is niet zozeer dat leren een begeleid proces is maar juist dat KM Quest een actieve en doelgerichte houding vergt. Wat betreft metacognitie is de conclusie dat het vooral van invloed is op leren wanneer er geen taakmodel aanwezig is. De meerwaarde van het taakmodel is dat het de acquisitie van taakkennis faciliteert en dat het de rol overneemt van metacognitieve vaardigheden. Deze dissertatie heeft het belang aangetoond van het taakniveau in het leren oplossen van problemen. Het feit dat de empirische resultaten kunnen worden geinterpreteerd met dat taakniveau levert ondersteuning voor het theoretisch model.
References Akhras, F. and Self, J. (2002). Beyond intelligent tutoring systems: situations, interactions, processes and affordances. Instructional science, 30:1–30. Anderson, J. (1983). The architecture of cognition. Harvard University Press, Cambridge, MA. Anjewierden, A., Shostak, I., and de Hoog, R. (2002). Kmsim: A meta-modelling approach and environment for creating process-oriented knowledge management simulations. In Proceedings of the 13th European Conference on Knowledge Acquisition, Management and Modelling (EKAW) Siguenza, Italy. Anzai, Y. and Simon, H. (1979). The theory of learning by doing. Psychological Review, 86:124–140. Artzt, A. and Armour-Thomas, E. (1992). Development of a cognitive-metacognitive framework for protocol analysis of mathematical problem solving in small groups. Cognition and instruction, 9:137–176. Ausubel, D., Novak, J., and Hanesian, H. (1978). Educational psychology; a cognitive view. Holt, Rinehart and Winston, NY. Baker, E. and Mayer, R. (1999). Computer-based assessment of problem solving. Computers in human behavior, 15:269–282. Bednar, A., Cunningham, D., Duffy, T., and Perry, J. (1992). Theory into practice: how do we link? In Duffy, T. and Jonassen, D., editors, Constructivism and the technology of instruction: a conversation. Lawrence Erlbaum Associates, Hillsdale, NJ. Breuker, J. (1994). A suite of problem solving types. In Breuker, J. and van de Velde, W., editors, CommonKads library for expertise modelling, pages 57–88. IOS Press, Amsterdam. Brown, A. (1978). Knowing when, where, and how to remember: A problem of metacognition. In Glaser, R., editor, Advances in instructional psychology. Lawrence Erlbaum Associates, Hillsdale, NJ. Brown, A. (1987). Metacognition, executive control, self-regulation and other more mysterious mechanisms. In Weinert, F. and Kluwe, R., editors, Metacognition, motivation and understanding, pages 65–116. Lawrence Erlbaum Associates, Hillsdale, NJ. Brown, A., Bransford, J., Ferrara, A., and Campione, J. (1983). Learning, remembering, and understanding. In Flavell, J. and Markman, E., editors, Handbook of child psychology: Vol. 2. Cognitive development, pages 77–166. Wiley, NY, 4th edition. 179
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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
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96 is a variable that influences th
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98 5.4 Discussion The hypothesis fo
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100 mal company results. In the fol
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102 more inclined to regulate domai
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104 these skills in order to solve
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106 Hypothesis 1: students acquire
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108 KMQUESTions of study II was per
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110 The second measurement of game
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112 T-PROS because they did not occ
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114 Category Rule Example Task Indi
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116 vised by one experimenter in or
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118 6.3 Results 6.3.1 Game results
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120 to Coltec. Only threats have a
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122 6.3.2.1 Effects of learning and
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124 Prospective Pre-test Post-test
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126 Distribution Frequency 1 high 2
Page 138 and 139: 128 No − model Model Retrospectiv
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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
Page 185 and 186: Samenvatting 175 gebruik van metaco
Page 187: Samenvatting 177 nificant leereffec
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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