The role of metacognitive skills in learning to solve problems

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138 in the model condition: Visualization handbook (T = 3.18, p < 0.01), Intervention handbook (T = 10.23, p < 0.01), KM handbook (T = 4.01, p < 0.01). Students in the model condition use more time for the Indicator handbook than students in the no-model condition (T = -3.94, p < 0.01). Students in the no-model condition spend almost a quarter of their time on the support elements in KM Quest, whereas students in the model condition spend less than 10% of their time on these elements. Finally, students in the no-model condition use the Visualizations for longer (T = 5.83, p < 0.01). As for the time spent in the phases of the KM model, students in the no-model condition do not use the phases FOCUS and MONITOR since these are not available to them. Students in the model condition spend a lot of time in these phases, especially in FOCUS. The content of the ORGANISE phase is different in the two conditions. In the no-model condition it only contains the step Select interventions. In the model condition, the step Decide on knowledge infrastructure is also available. It appears that students in the model condition use more time to go through both steps (T = -2.41, p < 0.05) than students in the no-model condition for the one step. Students use comparable amounts of time to effectuate interventions in the IMPLEMENT phase. In conclusion, these results reveal that students in the no-model condition possibly learn more efficiently because they do not have to go through the KM model. Students in the no-model condition use almost 80% of their time (approximately 3 hours) to chat, to use the Intervention handbook, the Visualizations and to use the ORGANISE phase. Students in the model condition use almost 80% of their time (approximately 4.5 hours) to go through all phases of the KM model and the chat (see also figure 6.6). Figure 6.6. Overview of majority of time spent on game elements per condition.
Study III: added value of the task model 139 As was shown earlier in this chapter, students in the model condition gain more KM model-specific procedural knowledge than students in the no-model condition but they score similarly on other post-test measures of learning and transfer. Students in the model condition mostly use the KM model to achieve these results whereas students in the no-model condition rely on other elements of the learning environment. Students in the model condition need to regulate their use of the KM model whereas students in the no-model condition do not need to do this. When students in the no-model condition use metacognitive skills, it has a positive effect on learning outcomes. The KM model appears to restrain this relation. In order to explore the data further, additional correlations were calculated. Figure 6.7 gives an overview of the relation among the amount of time spent on the elements in KM Quest, results on post-tests of knowledge and transfer, and concurrent measurement of metacognition. Bold arrows indicate that positive significant correlation coefficients exist whereas dotted arrows indicate that a negative significant correlation coefficient exists. In both conditions, a negative relation exists between the support elements of KM Quest and learning. Apparently, spending more time in these elements results in lower post-test results. Perhaps merely reading information in the support elements is not sufficient for learning, but actively using the information (i.e. in the ORGANISE phase which is positively related to KM model-specific procedural knowledge) is. In the model condition, spending time in the FOCUS phase of the KM model is positively related to the use of metacognitive skills, for the IM- PLEMENT phase a negative relation exists. In the no-model condition, the ORGANISE phase is positively related to both knowledge acquisition and the use of metacognitive skills. IMPLEMENT is correlated negatively with knowledge acquisition. A suggestion is that players in the no-model condition analyse the event and discuss possible ways to solve the problem in the events in the ORGANISE phase whereas students in the model condition are required to do this in the FOCUS phase. Finally, it is remarkable that the MONITOR phase does not appear to have any effect on learning and the use of metacognitive skills. In this phase in the KM model players are required to reflect on the results achieved in Coltec after each event and to write a yearly report in which they reflect on the overall problem solving process after having played four events. In conclusion this analysis shows that only the activities belonging to the IMPLEMENT phase are related to learning, under the condition that
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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
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 144 and 145: 134 Condition * Metacognition G MET
Page 146 and 147: 136 Figure 6.5. Relations between m
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
Page 195: REFERENCES 185 van Harmelen, F., Wi
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Human-Computer Interaction and Pres
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