The role of metacognitive skills in learning to solve problems

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128 No − model Model Retrospective Pre-test Post-test Pre-test Post-test Declarative MS− .51 .16 13 .63 .13 13 .49 .08 11 .63 .11 11 MS+ .52 .09 10 .64 .09 10 .50 .12 12 .59 .08 12 Specific procedural MS− .41 .20 13 .56 .14 13 .50 .16 11 .71 .12 11 MS+ .48 .10 10 .57 .10 10 .54 .16 12 .69 .10 12 General procedural MS− .47 .11 13 .62 .09 13 .50 .14 11 .64 .10 11 MS+ .51 .14 10 .55 .11 10 .56 .13 12 .56 .08 12 Transfer MS− .71 0.07 13 .67 0.11 10 MS+ .72 0.07 11 .71 0.07 12 Table 6.13. Mean and standard deviation of the pre- and post-test scores on declarative knowledge measured in proportion of correct answers. Retrospective measurement of metacognition. Condition * Metacognition Prospective Retrospective Declarative F = 1.03 p = 0.32 F = 0.11 p = 0.64 Specific procedural F = 0.37 p = 0.55 F = 0.26 p = 0.62 General procedural F = 0.66 p = 0.42 F = 0.09 p = 0.76 Transfer F = 0.35 p = 0.56 F = 0.38 p = 0.54 Table 6.14. F-value and significance of the between subjects interaction effect of Condition * Metacognition for both test-takings of the MSLQ. For the individual conditions, no significant correlation coefficients exist between time-on-task versus declarative, specific or general procedural knowledge or transfer. For the first three measures, effects of the pre-test scores were partialed out. In the model condition no relation exists between total time spent playing KM Quest and learning measures, nor in the no-model condition. Only if time-on-task correlates significantly with post-test measures of learning in the individual conditions, could it be used as a covariate (Stevens, 2002). Since this is not the case, the conclusion is that in this study time-on-task is not related to learning results. Therefore, the fact that students in the model condition acquire more KM model-specific procedural knowledge than students in
Study III: added value of the task model 129 the no-model condition is not related to having spent more time in order to finish the game. 6.3.2.4 Correlation between learning and self-reported use of metacognition No-model condition 1 2 3 4 5 6 Declarative (1) - .34* -.04 .46* .33 .27 Specific procedural (2) - -.11 .43* .04 -.17 General procedural (3) - .43* -.18 -.08 Transfer (4) - -.21 .00 Prospective MS (5) - .74** Retrospective MS (6) - Table 6.15. Correlation coefficients between post-test measures of knowledge and transfer and self-reported use of metacognitive skills (* p < 0.05; ** p < 0.01, onetailed). Model condition 1 2 3 4 5 6 Declarative (1) - .63** .24 .71** .02 -.04 Specific procedural (2) - .00 .52** .23 .09 General procedural (3) - .37* -.07 -.28 Transfer (4) - .21 .11 Prospective MS (5) - .71** Retrospective MS (6) - Table 6.16. Correlation coefficients between post-test measures of knowledge and transfer and self-reported use of metacognitive skills (* p < 0.05; ** p < 0.01, onetailed). In order to do justice to the initially continuous data available on the pro- and retrospective measurements of metacognition, correlational analysis were performed on these data. The relation between learning success and self-reported use of metacognitive skills was explored. Results are presented in table 6.15 and table 6.16. One result that stands out is the fact that the scores for declarative knowledge are strongly related to those on KM model-specific procedural knowledge. This effect is stronger in the model condition than in the no-
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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
Page 87 and 88: Study I: Pilot 77 Figure 4.4. 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 134 and 135: 124 Prospective Pre-test Post-test
Page 136 and 137: 126 Distribution Frequency 1 high 2
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
Page 185 and 186: Samenvatting 175 gebruik van metaco
Page 187 and 188: 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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