The role of metacognitive skills in learning to solve problems

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154 havioural measures prove to be quite a labour-intensive way of collecting data, this method remains to be preferred over self-report questionnaires when a predictive quality for learning is deemed important. The empirical studies of this research project indicate that the selfreport measures do not correlate with the protocol measures of metacognition. Three factors play a role in interpreting these results. First one has to take the timing of the measurement into account (concurrent versus non-concurrent). Second, the nature of the measurements differs (indirect versus direct). And third, the concurrent measure is indeed an individual measure but it is taken in a collaborative context. The lack of concordance in this study is difficult to interpret. Future work concerning metacognition could be related to effects of collaborative learning versus individual learning. Future studies could check the assumptions put forward in this thesis concerning the processes measured by the MSLQ and protocol analysis. It could also be investigated to what extent differences exist between individual and socially-shared metacognition. Research in this area is scarce (Iiskala, Vauras & Lehtinen, 2004). The effects of a collaborative setting on metacognition might turn out to be in line with the ideas of distributed cognition, especially in technology-rich environments (Hollan, Hutchins & Kirsh, 2000). Researchers in this area argue that in groups, cognitive processes may be distributed (Salomon, 1993). In that sense, it is very likely that metacognitive processes are distributed across team members and that one team member may take up the role of the ‘reflector’. Future research could also shed light on the perhaps related ‘free-rider’ phenomenon. 7.4 The value of a task model Regarding the third and last research question, the added value of the task model is investigated by contrasting a no-model with a model condition in the third empirical study. The benefit of the task model for knowledge acquisition is that students in the model condition reach higher scores for KM model-specific knowledge than students in the nomodel condition. Students in both conditions do not differ from each other on the other types of knowledge nor on the transfer test. However, students in the model condition take almost twice as long in the game environment as students who do not have the model available. This additional time is mostly spent in getting to know the model and in learning to use it for solving problems. In conclusion, the task model only has a limited influence on knowledge acquisition in KM Quest, and it requires high investments of time and effort.
Conclusions 155 Another effect of the task model that is suggested by the results is that it replaces the need to use metacognitive skills. The correlational results in study III (chapter 6) shows that when the task model is present in KM Quest, metacognitive activities are not related to learning. When the model is not available, metacognitive activities are related to knowledge acquisition. These results support the theoretical model. KM Quest represents a simulated reality of one typical business case. The ultimate benefit of the KM model may have to be proven in reallife situations in which much more multi-facetted KM problems exist. Having a systematic approach to solving such problems should ideally benefit not only how one deals with these problems but also the quality of the solution found. This is worth further investigation. Another line of research could focus on transforming the task model into a learning model. Instead of prescribing how a particular class of problems should be solved by identifying relevant problem solving activities, such a learning model could indicate the rationale for the distinguished problem solving activities. This could include giving insight into the benefit of specific activities for the learning and problem solving process and indicating what one could learn from a typical activity in the light of the overall problem solving process. In that sense the task model is transformed into a meta model. Then, students might possibly be more inclined to develop their own (intuitive) task model. In conclusion, this thesis has shown the importance of identifying a task-level in the learning and problem solving process. The fact that the empirical results can be interpreted by the introduction of a task level gives support for the theoretical model for learning to solve problems.
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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
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 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 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
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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