The role of metacognitive skills in learning to solve problems

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40 for answering the research questions formulated in chapter 1. It is argued that KM Quest is a learning environment that embodies the principles of constructivism. Also it is shown that the KM model in KM Quest comes close to the notion of a task model as explained in chapter 2, which permits investigation of the effects of such a task model on learning to solve problems and the use of metacognitive skills. This chapter consists of two parts. The starting point for the design of KM Quest was the Coltec case material (de Hoog et al., 1999). This paper-based case material is initially used in an exploratory study that investigated what one can learn from a paper-based game setup in order to develop the electronic learning environment KM Quest. The question is whether a gaming approach is beneficial for learning how to solve KM problems and whether players are able to propose appropriate solutions for KM problems. The second part of this chapter concerns the description of KM Quest, which was developed in the European project KITS. 3.2 The formative study 3.2.1 The domain of Knowledge Management Knowledge Management (KM) nowadays is a booming business. KM is about managing business initiatives that involve knowledge in such a way that a company can benefit from it by, for instance, gaining more profit or market share. This is more difficult than it seems at first sight because knowledge has several properties that make it a difficult resource to manage (Wiig, de Hoog & van der Spek, 1997). For one, knowledge is intangible, it is difficult to measure. Second, creating or producing knowledge is time-consuming. Third, it is incorporated in persons with a will. Advantages of knowledge as a resource that has to be managed are that it can increase while being used and it can be used in multiple (business) processes at the same time. Effective knowledge management requires that a systematic approach is taken in order to achieve good business results. Wiig et al. (1997) propose such a systematic approach in the form of a Knowledge Management model. This model is depicted in figure 3.1. Solving KM problems is seen as a cyclic process that occurs at two levels, the management- and the object-level. At the upper, management level, it distinguishes management activities that are related to an analysis of the knowledge household of the organisation. At the lower object-level, classes of basic knowledge operations are identified that are aimed at solving the KM problem.
KM Quest 41 Figure 3.1. The Knowledge Management model adapted from De Hoog et al. (1999). The four management activities are: REVIEW, CONCEPTUALIZE, REFLECT and ACT. REVIEW: check what has happened in the past and what the current status of the company is in terms of business indicators. CONCEPTUALIZE: analyse the strong and weak points in terms of knowledge processes and domains, and suggest improvements. REFLECT: select optimal plans in order to solve bottlenecks, and consider points of attention when implementing the plans. ACT: execute the proposed and selected plans. In the ACT-activity one executes the proposed plans at the objectlevel. Four classes of basic knowledge operations can be distinguished at the object-level: developing, consolidating, distributing and combining knowledge. These are called classes of interventions because they represent classes of solutions for typical KM problems.
Page 1 and 2: The role of metacognitive skills in
Page 3 and 4: The role of metacognitive skills in
Page 5 and 6: Contents 1. INTRODUCTION 1 2. THEOR
Page 7: Contents ix 6. STUDY III: ADDED VAL
Page 10 and 11: xii Rienke Schutte, zonder de medew
Page 12 and 13: 2 in realistic settings and everyda
Page 14 and 15: 4 use a trade-off between efforts i
Page 16 and 17: 6 of this thesis are described. In
Page 18 and 19: 8 First an overview of relevant lit
Page 20 and 21: 10 In research concerning reading a
Page 22 and 23: 12 problem has actually been found
Page 24 and 25: 14 or whether domain specific metac
Page 26 and 27: 16 learners should be able to artic
Page 28 and 29: 18 piece of origami paper. Both mat
Page 30 and 31: 20 tion given to the problem solver
Page 32 and 33: 22 is rather directive. It does not
Page 34 and 35: 24 The problem solver has declarati
Page 36 and 37: 26 problem and the fact that studen
Page 38 and 39: 28 learn from them. In terms of Jan
Page 40 and 41: 30 This model is called DORMOBILE (
Page 42 and 43: 32 Second, the meta-level in proble
Page 44 and 45: 34 Figure 2.8. Theoretical model of
Page 46 and 47: 36 task-level. In order to keep the
Page 49: Chapter 3 KM QUEST The goal of this
Page 53 and 54: KM Quest 43 the fact that some plan
Page 55 and 56: 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
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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
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116 vised by one experimenter in or
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118 6.3 Results 6.3.1 Game results
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120 to Coltec. Only threats have a
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122 6.3.2.1 Effects of learning and
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124 Prospective Pre-test Post-test
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126 Distribution Frequency 1 high 2
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128 No − model Model Retrospectiv
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130 model condition. Perhaps, perfo
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132 Cog Tas Meta Total No-model con
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134 Condition * Metacognition G MET
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136 Figure 6.5. Relations between m
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138 in the model condition: Visuali
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140 Figure 6.7. Overview of relatio
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142 ‘monitoring’. Prospective s
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144 learning. Finally, in the no-mo
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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
Page 167 and 168:
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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