The role of metacognitive skills in learning to solve problems

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110 The second measurement of game result of the teams was the status of the knowledge map in the 7th quarter. The knowledge map is one of the most important visualizations of the status of the knowledge household of Coltec since it presents a concise overview of the status of all properties of knowledge processes in the knowledge domains. An example of the knowledge map is given in figure 6.3. The three squares represent the three knowledge departments in KM Quest: the R&D, Marketing and Production department. For each of these departments, five knowledge processes are shown (see Notes on the legend), being Retention, Gaining, Development, Utilisation and Transfer of knowledge. For all of these knowledge processes, the overall effectiveness is indicated in terms of a colour visualization. Darker colours depict more effective processes. For all knowledge processes but Retention, the speed is indicated in terms of a letter combination. Faster processes indicate a faster pace at which the knowledge process is taking place. Students were instructed to manage the knowledge household of Coltec as well as possible. Thus, they should aim for the most effective and fastest knowledge processes. The status of the knowledge map may be a good indicator of the game result of teams. For analysis purposes, the qualitative aspects represented in the knowledge map (colour and letter combinations) were turned into quantitative information. The total number of optimal processes for both speed and effectiveness was taken into account. For instance, in the knowledge map in figure 6.3, 8 out of 12 processes are very fast (vf) which is 66.67% and 13 out of 15 processes are most effective, which is 86.67%. The average of these two percentages (76.67) reflects the overall percentage of optimal processes. 6.2.3.3 Metacognitive skills The scale metacognition of the MSLQ was used again in this study. The content and procedure was equivalent to the previous studies. Students took identical test versions of the MSLQ before and after the game in order to measure a shift of self-reported strategy use. The pre-test concerned filling in the items in general, The post-test involved filling in the items having in mind how one has just played KM Quest. These measurements represent a prospective and a retrospective measurement of metacognition. The chat protocols formed the basis for the concurrent measurement. The chat protocols contained all the communication that took place between team members, since students were instructed to only communicate through the game’s chat facilities.
Study III: added value of the task model 111 Figure 6.3. Example of a knowledge map of team 3 in the 7th quarter. The coding scheme developed in the pilot study (chapter 4) was used to annotate contributions of team members. The criteria for the coding scheme were (1) categories do not overlap (exclusive categories) and (2) categories cover all possible types of contributions in the protocols (exhaustive categories) (Eggen & Sanders, 1993). Discussion about the annotation process between two coders led to several changes to this original coding scheme. The category TASK was introduced. This category is used for annotating the beginning and ending of concrete activities. It concerns the regulation of performing domain activities This category is directly related to the task-level in the theoretical model (chapter 2). It is neither domain-independent nor cognitive, but task-related. PLAN is different from TASK in the sense that the former concerns the intention or proposal to perform activities in a systematical order. The category PLAN is more abstract in nature than TASK. All categories except COGNITIVE and TASK are considered to be metacognitive in nature. Several categories were discarded namely G- STRA, G-PRED, G-PLAU, T-STRA, T-PRED, T-PLAN, T-PLAU and
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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
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 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 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 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
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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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