The role of metacognitive skills in learning to solve problems

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134 Condition * Metacognition G META KM META Declarative F = 0.00 p = 0.95 F = 0.01 p = 0.92 Specific procedural F = 0.37 p = 0.55 F = 0.13 p = 0.72 General procedural F = 2.62 p = 0.11 F = 0.00 p = 0.99 Transfer F = 0.06 p = 0.81 F = 0.64 p = 0.43 Table 6.19. F-value and significance of between-subject interaction effects between condition and metacognition for declarative, specific and general procedural knowledge and transfer. respect to the previous analyses since the continuous variables for metacognition were used (see also section 6.3.2.2). In the model condition no significant correlations exist between categories of the coding scheme, self-report measures of metacognition and learning results. In the no-model condition a positive significant correlation exists between KM COG and transfer (r = 0.45, p < 0.05). In the same condition, a positive significant correlation exists between G META and declarative knowledge (r = 0.50, p < 0.05) and between KM META and KM model-specific procedural knowledge (r = 0.37, p < 0.05). The self-report measures of metacognition do not correlate with concurrent measures of metacognition in either condition. The analysis of variance presented in section 6.3.3.2 shows a main effect of KM META on model-specific procedural knowledge. The correlational analysis shows a positive correlation between KM META and this type of knowledge but only in the no-model condition. Thus the correlation analysis points in the same direction as the findings of the analysis of variance. This partly supports hypothesis 3 and 4 that posit an interaction effect between the use of metacognitive skills and learning. The other two significant correlation coefficients cannot be related to the outcomes of the analysis of variance in the previous section since no main effects are found for G META on declarative knowledge. The relation between KM COG and transfer was not subject to analysis in the GLM. One explanation for this is that in the analysis of variance, the independent variables for metacognition were transformed from a ‘naturally occurring’ continuous variable into a dichotomous variable which results in loss of information compared to the correlation analysis. In the previous section it was suggested that the nature of the metacognitive skills used in either condition could be different. In section 6.3.3.1 some evidence was found that students in the model condition mainly use metacognitive skills to regulate the use of the KM model in
Study III: added value of the task model 135 order to solve problems. This is not related to knowledge acquisition as is shown in this section. Students in the no-model condition use metacognitive skills to regulate the learning process. For them, the use of metacognitive skills is related to knowledge acquisition. Hypothesis 5 states that concurrent measures of metacognition such as protocol analysis are more related to learning than indirect measures. The results of this study partly support this hypothesis. It appears that both measures of metacognition from the protocol analysis (G META and KM META) are significantly related to learning success but only in the no-model condition. When the KM model is present in the learning environment, no such correlations exist. Hypothesis 7 concerns a positive relation between metacognition and transfer. The results concerning this hypothesis are presented in detail in figure 6.5. This study cannot confirm that a relation exists between concurrent measurement of metacognition and transfer. Instead, in the no-model condition, the amount of discussion related to cognitive activities (KM COG) appears to positively influence transfer. Also, the amount of metacognitive contributions (either G META or KM META) that students make in their communication is positively related to the acquisition of knowledge. Earlier (see table 6.15) it was shown that knowledge acquisition is related to the ability to transfer knowledge in the no-model condition. It appears that the use of metacognitive skills is not directly related to transfer but that knowledge acquisition mediates or influences this relation. In the model condition no relation is found between any category of the coding scheme and the types of knowledge assessed with KMQUESTions and the transfer test. Concurrent measurement of (meta)cognitive activities is not related to learning. The relations between the different types of knowledge in this condition have already been presented in table 6.16 on page 129. 6.3.4 Additional measures In order to gain insight into differences in how students use KM quest in both conditions, ANALOG was used to analyse the content of the log files. In table 6.20 the average percentage of time spent on important elements per student was calculated. The element ‘Other’ represents activities such as Help and spending time in the start page of KM Quest (Office). Students in the model condition spend almost twice as long playing KM Quest as students in the no-model condition (respectively 5 hours, 29 minutes and 22 seconds versus 3 hours, 52 minutes and 32 seconds).
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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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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 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 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
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
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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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