The role of metacognitive skills in learning to solve problems

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126 Distribution Frequency 1 high 2 low 6 2 high 1 low 5 0 high 3 low 2 3 high 0 low 1 1 high 1 low 1 2 high 0 low 1 Total 16 Table 6.11. Distribution of high and low scorers in teams on self-reported metacognitive skills measured retrospectively. more frequent use of these skills (and gain low to average scores on the knowledge test) should have a more active role in solving the KM problems. The protocol of team 86 in the model condition was investigated more closely in this respect. Limited evidence is found for this assumption. One student in this team appears to be rather passive. This student mostly writes contributions that focus on speeding up the problem solving process such as “Verder” [Continue] or “Wat maakt het ook uit, kom maar verder” [what does it matter, come on, let’s continue]. This student self-reports the less frequent use of metacognitive skills and he reaches quite a high post-test score on general procedural knowledge (0.72). The active student is the one who writes the yearly report (an obligatory ‘reflective’ moment in quarter 5) and this student writes contributions that indicate reflections on the problem solving process. He comments, for instance, that an event-related strategy is possibly not sufficient for managing the knowledge household optimally. He also gives some explanations for results found. This student actually reports that he uses metacognitive skills to a greater extent than the former student. This limited qualitative investigation shows that the ‘free-rider’ hypothesis could be true (Kerr & Bruun, 1983). This hypothesis could be investigated more thoroughly in future research. As for the hypothesized between-subjects interaction effect concerning condition and metacognition (see hypotheses 3 and 4) table 6.12 and table 6.13 give an overview of the descriptive statistics. The results in table 6.14 show that no significant interaction effects exist regarding between-subject effects.
Study III: added value of the task model 127 Finally, no main effects of self-reported use of metacognitive skills on the knowledge tests nor the transfer test in either condition exist. No − model Model Prospective Pre-test Post-test Pre-test Post-test Declarative MS− .50 .15 12 .61 .12 12 .50 .07 12 .63 .10 12 MS+ .53 .11 10 .68 .10 10 .51 .14 9 .60 .09 9 Specific procedural MS− .40 .19 12 .54 .13 12 .51 .13 12 .71 .08 12 MS+ .50 .12 10 .59 .11 10 .56 .18 9 .73 .10 9 General procedural MS− .47 .14 12 .61 .08 12 .50 .14 12 .60 .12 12 MS+ .50 .11 10 .57 .13 10 .59 .12 9 .59 .07 9 Transfer MS− .70 .08 12 .71 .07 12 MS+ .68 .11 10 .72 .06 9 Table 6.12. Mean and standard deviation of the pre- and post-test scores on declarative knowledge measured in proportion of correct answers. Prospective measurement of metacognition. In conclusion, the role of metacognition remains unclear when one focuses on these self-report measures. Merely one interaction effect is found in which the self-reported use of metacognitive skills after task performance appears to influence general procedural knowledge acquisition. Students who report less use of metacognitive skills in KM Quest show the largest knowledge gain with respect to students who report more use of metacognitive skills in KM Quest. This is in contrast with hypotheses 3 and 4 that posit an interaction effect between condition and metacognition. The results of the concurrent measurement of metacognition in the light of these hypotheses are discussed in section 6.3.3.1 6.3.2.3 Time-on-task as a confounding factor Although this study was set up in such a way that participants in both conditions should spend comparable amounts of time playing KM Quest, an analysis of the log files revealed that students in the model condition spend more time than students in the no-model condition (see also section 6.3.4). Mean total time spent of students playing KM Quest in the no-model condition is 3 hours, 52 minutes and 32 seconds. Team members in the model condition spend on average 5 hours, 29 minutes and 22 seconds. Perhaps students in the model condition use their additional time to acquire the KM model-specific procedural knowledge.
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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
Page 85 and 86: Study I: Pilot 75 Figure 4.2. 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 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 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
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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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