The role of metacognitive skills in learning to solve problems

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98 5.4 Discussion The hypothesis formulated for this study is that students acquire both declarative and procedural knowledge from playing KM Quest. The results indicate that this hypothesis can be accepted. The results also show that they acquire both types of knowledge to the same extent. Thus, students show that they are capable of meaningful learning in a constructivist learning environment. This is contrary to what the results of other empirical research on games and simulation indicated as discussed in chapter 1. One possible explanation for the positive learning effect is the fact that students are provided with a task model for solving KM problems. The KM model is a compiled model for solving Knowledge Management problems that is decomposed into subtasks and activities. One effect of the task model in KM Quest could possibly be that it requires the application of declarative knowledge that is available at the object-level in the activities. This supports learning. Procedural knowledge concerns knowledge about how to use the KM model. Evidently, using the model leads to an increase in procedural knowledge. The exploratory analysis does not corroborate these explanations in terms of a firm relationship between knowledge acquisition and the amount of time spent using the KM model. But a more systematic comparison of learner behaviour in a no-model environment with such behaviour in a model environment should give more insight into the effects of a task model. In chapter 6 the effect of the task model is investigated more thoroughly. The exploratory analyses with respect to metacognition as measured by the MSLQ and learning outcomes do not show any relations. Apparently, self-reported metacognitive skills are not related to achieving good learning results. There are however a number of possible interpretations of this result. First of all, there is the trait versus state distinction in metacognition (Hong, 1998). This distinction resembles the domain generality versus domain specificity dimension as discussed by several researchers (Schraw, 1998; Sternberg, 1998; Veenman et al., 1998). Metacognitive skills in terms of a trait can be seen as a disposition to react or behave in a specific, predictable manner that is stable across situations. Domain general metacognitive skills represent the fact that the pattern of using metacognitive skills in different domains is comparable. In the current study, metacognitive skills are measured in a domain-independent way, in terms of a trait measure. There is a debate on how expertise develops, especially on the development of metacognitive skills (see chapter 2). On the one hand, Veen-
Study II: learning revisited 99 man (1997) and Prins (2002) argue that metacognitive skills of novices in a particular domain are general in nature. Only when they become experts do the domain-independent metacognitive skills become domainspecific task schemas. This line of argument would be corroborated by a significant relation between the general use of metacognitive skills and knowledge acquisition. This is however not the case in this study. Thus perhaps the opposite is true. Schraw (1998) argues that metacognitive knowledge and skills are domain- or task-specific initially. When students cover more domains and develop more expertise, they may generalise their metacognitive knowledge and skills across domains. Possibly, a state measure of metacognition that is specific for a domain, is a better predictor of learning results, especially when it concerns novice learners in a particular domain. This could however not be established in the pilot study. However, the results of this study are based on a rather small subsample. The following study will include both a prospective and a retrospective measurement of metacognition in order to investigate the relation between different measures of metacognition and knowledge acquisition. Second, there is a methodological explanation for why no relation between the use of metacognitive skills and learning success is found. As is argued in chapter 6, the best way of measuring what metacognitive skills people use when learning to solve problems is to observe behaviour. Since this is however a rather time consuming activity, the preference exists for using prospective or retrospective questionnaires. The problem with these questionnaires is that they are self-report measures. What people report they are doing does not necessarily coincide with their actual behaviour. Therefore in the following study a concurrent measure will be employed in the form of protocol analysis. The coding scheme that is developed in the pilot study will be used to score the chat protocols. Third, the KM model in KM Quest is presented as a model belonging to the task-level. While using this model, students possibly no longer need to revert to metacognitive skills in order to learn to solve KM problems, instead they have the opportunity to use the KM model (see chapter 2). This could explain why no relation is found between the self-reported use of metacognitive skills and learning success. This calls again for contrasting a no-model version of KM Quest with the standard environment in which the KM model is present. Additionally, regarding game results, this study shows that apparently it makes no difference for the overall status of Coltec whether one chooses interventions randomly or consciously. This is not optimal feedback for players. Interventions suited for a particular event should lead to opti-
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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
Page 57 and 58: KM Quest 47 Quest. They are visuali
Page 59 and 60: KM Quest 49 cesses in Coltec. For e
Page 61 and 62: KM Quest 51 Figure 3.5. The Knowled
Page 63 and 64: KM Quest 53 3.3.1.3 The IMPLEMENT p
Page 65 and 66: KM Quest 55 process in an electroni
Page 67 and 68: Chapter 4 STUDY I: PILOT This chapt
Page 69 and 70: Study I: Pilot 59 the spontaneous o
Page 71 and 72: Study I: Pilot 61 Also, the questio
Page 73 and 74: Study I: Pilot 63 however, to what
Page 75 and 76: Study I: Pilot 65 ceived 12 questio
Page 77 and 78: Study I: Pilot 67 objective, a seco
Page 79 and 80: Study I: Pilot 69 other. It also ex
Page 81 and 82: Study I: Pilot 71 In conclusion, th
Page 83 and 84: 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 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 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
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148 interpreting the results on met
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150 When no task model is available
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152 Another perhaps more valid expl
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154 havioural measures prove to be
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Appendix A Solving a problem in KM
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APPENDIX A: Solving a problem in KM
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APPENDIX A: Solving a problem in KM
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164 to learning to solve problems.
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166 cognition in a constructivist l
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168 forward. The most important one
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Chapter 9 SAMENVATTING Het huidige
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Samenvatting 173 De aanwezigheid va
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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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