The role of metacognitive skills in learning to solve problems

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112 T-PROS because they did not occur in the protocols. In order to present data, for GROUP, TOOL and KM three measures were calculated, namely COGNITIVE, TASK and META. The measure META consists of all activities that are scored as metacognitive. The final coding scheme is shown in figure 6.4. Figure 6.4. Coding scheme used to label the contributions from the chat protocols. The annotation process also led to the generation of many specific rules applicable to the coding categories and examples of appropriate contributions for each category that was created. In table 6.2 the most important rule for each category is given including a prototypical example of a contribution for the particular category. One important additional rule was that the category GROUP always overrules the categories TOOL and KM. Thus, a contribution in the form of a question was always coded as GROUP. This was done in order to specifically pay attention to the use of metacognitive skills in a collaborative context.
Study III: added value of the task model 113 In order to check reliability, half of a protocol of one team was coded independently by two coders. The objective was that both coders would reach sufficient agreement in their annotation of the protocol. Protocols were not segmented in fragments as was done in the pilot study (see chapter 4) because of the sheer number of contributions that have to be coded manually (17,182 contributions in total). Segmentation would increase the number of fragments to be coded even more. Instead when multiple categories were applicable to a single contribution, the category for the longest part of the contribution was used. In order to calculate inter-observer agreement, Cohen’s Kappa was used, which presents a proportion of agreement adjusted for chance. A Kappa of 0.60 and higher reflects sufficient observer agreement. Initially, a Kappa of 0.43 was found. After discussion, both coders annotated the second half of the protocol independently and reached a Kappa of 0.70, which reflects sufficient inter-observer reliability. Category Rule Example GROUP A direct referral to a “What do you think team member or a question about this, Henry?” Cognitive Asking the opinion of other “How about intervention team members Task Imperative concerning the initiation or finishing of activities Reflect on problem Comprehension monitoring Planning Monitoring Evaluate problem solving A content related reflection on the nature of a problem Indications of lack of understanding without a clear KM component Indications of how one wants to go about solving the problem Indications of agreement between team members Indication of evaluating the overall group process in relation to problem solving TOOL Any contribution related to the GUI, functionality or affordances of KM Quest Cognitive Descriptive contributions concerning the GUI, functionality or affordances of KM Quest I6?” “You start filling in the goals!” “But is this a KM-related problem?” “But what do we have to do now?” “Do we have any other goals to pursue?” “Ok, I agree!” “But not all of our team members have made the same effort in playing the game” “In the home page of KM Quest you see the laptop on the desk” “In the intervention book there are other things we can do”
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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
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 120 and 121: 110 The second measurement of game
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
Page 171: 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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