The role of metacognitive skills in learning to solve problems

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166 cognition in a constructivist learning environment and (2) to develop a concurrent measurement instrument for metacognition and to develop an analysis tool for process measures of learning. The main hypothesis is that KM Quest supports meaningful learning. Furthermore it is assumed that metacognitive behaviour occurs in the communication protocols of team members so that it can be annotated. This permits the development of an alternative, concurrent measure of metacognition. Forty-one students from the ‘Hogeschool Zuyd’ in Maastricht participated in the pilot study in Autumn 2002. Only 27 reached the 7th quarter in the game. Data from those who did not reach the 7th quarter were discarded. A ‘paper and pencil’ questionnaire was developed that measures transfer of knowledge. Transfer only occurs if meaningful learning has taken place. This questionnaire involves applying knowledge and skills learned in KM Quest to a slightly different case (HollandSky). The use of metacognitive skills was measured retrospectively, by means of the scale Metacognitive self-regulation of the MSLQ. A coding scheme was developed in order to explore whether metacognitive activities occur in the collaborative chat protocols. The results of the HollandSky test indicate that there is some evidence that students are capable of low-road transfer of knowledge. This evidence however is not very strong since the open-question format results in a lot of missing data, especially in the post-test. Furthermore, the self-report questionnaire indicates that students only marginally agree with having used metacognitive skills in KM Quest. No significant correlations exist between the transfer test and the self-reported use of metacognitive skills. The development of the coding scheme shows that next to cognitive contributions, a variety of metacognitive contributions exist in the protocols which can be coded with the categories of metacognitive skills described in the theoretical model. Furthermore the protocols show that besides domain-related contributions, contributions related to groupdynamics, and functionality and affordances of the game environment exist. A final outcome of the this pilot study concerns ANALOG. The development of this tool occurred simultaneously with the data analyses. Its main contributions are that game sessions of teams can be replayed and it supports annotation of the chat protocols. This analysis tool provides the opportunity to efficiently gain insight into the learning process based on log file data. In conclusion, the pilot study has presented the opportunity to develop instruments and tools that measure learning and the use of metacognitive skills in KM Quest.
Summary 167 In chapter 5 the results of the second study are reported. The main aim is to gain sufficient evidence that KM Quest enables meaningful learning since only weak evidence is found in the pilot study. Also the use of metacognitive skills is explored, this time prospectively. Thirty-one students from the ‘Hogeschool Zuyd’ participated in the study in Autumn 2003. The group was comparable to the group in the pilot study in all respects. An electronic test was developed that measures meaningful learning (KMQUESTions). It consists of a large number of multiple-choice questions related to the learning goals of KM Quest, namely the acquisition of declarative and procedural knowledge. The MSLQ was used again in order to explore the self-reported use of metacognitive skills. This time, it was administered before students started playing KM Quest. The results indicate that students acquire declarative and procedural knowledge as a result of playing KM Quest. Thus, students show that they are capable of meaningful learning in a constructivist learning environment such as KM Quest. The measurement of metacognition shows that students self-report only a very low level of use of metacognitive skills in general. The scores of this prospective measurement do not differ a lot from the retrospective measurement performed in the pilot study. Furthermore, no relation exists between the prospective measurement of metacognition and learning success. The conclusions of this study are twofold. First, constructivist gamingsimulation environments such as KM Quest can support meaningful learning. The suggestion is put forward that this may be due to the fact that the environment contains a task model. In order to find empirical support for this, it is proposed that in the third study, a model versus a no-model condition design is used to investigate the effects of such a task model. Second, the question is posed whether a (prospective or retrospective) self-report questionnaire is the most appropriate way of measuring the use of metacognitive skills. It is proposed to use a direct behavioural measure such as protocol analysis in the third and last study in order to gain better understanding of metacognition. In chapter 6 the added value of the task model is investigated with respect to meaningful learning and the use of metacognitive skills. To that purpose two conditions were created. In the no-model condition, students work with KM Quest that does not contain the KM model. In the model condition students work with the standard version of KM Quest in which the KM model is present. Several hypotheses are put
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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
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104 these skills in order to solve
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106 Hypothesis 1: students acquire
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108 KMQUESTions of study II was per
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110 The second measurement of game
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112 T-PROS because they did not occ
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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 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 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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