The role of metacognitive skills in learning to solve problems

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56 given in the next quarter about the type of events just played and the possible interventions one should have chosen. An Advisor is activated when the value of certain knowledge indicators drops below a threshold value. This is an indication that Coltec is not performing well. If this is the case, then advice is given with respect to the types of interventions one could choose in order to increase the values of the indicators mentioned. For instance, when the status of the indicators for properties of knowledge development processes in the production department has dropped below 4.5 then advice is given to focus on training people and implementing organisational changes. Reflection is supported in the MONITOR phase of the KM model. Players are required to write a yearly report that focuses on what problems were encountered, how these problems were solved and what went well and what went wrong. This enables reflecting on the problem solving process and identifying strong and weak points. The game is strongly geared towards collaboration. Players are organised in teams of three members. They communicate with each other in a central chat facility (the telephone in the office). All activity templates in the KM model have a chat facility available, too. This supports topic-related communication. The activities in the KM model require collaboration. Collaboration is supported by the presence of shared work spaces. Players, for instance, have to choose a focus on specific knowledge departments or processes or they have to formulate an adequate interpretation of an event which requires free-text writing areas. In order to implement interventions players have to agree on a set of interventions decided upon earlier in the KM model. This is supported by an electronic voting system. Once all players agree on a (set of) interventions, the game progresses to the next quarter in which a new event is presented. In order to keep track of each other in the game, a special function called Group call is implemented in KM Quest. Players can invite each other to a specific location in the game. The invitation can be accepted or rejected. Acceptance results in a ‘tele-transportation’ of a player from a current location to the desired location. This allows teams to gather quickly. In conclusion, KM Quest is designed in such a way that in theory it is a suitable learning environment that is consistent with the constructivist paradigm. So, this environment is used in the empirical studies described in the next three chapters.
Chapter 4 STUDY I: PILOT This chapter 1 describes the results of the pilot study of KM Quest. The study focuses on exploring meaningful learning and the use of metacognitive skills in KM Quest, and the development of measurement instruments and analysis tools. Twenty-seven students participated in this study. Learning results were measured by means of pre- and post-tests for transfer of knowledge, which is an indication that meaningful learning occurs. Metacognitive skills were measured by means of the MSLQ. After the exclusion of cases with missing data, results of the subsample (N = 14) reveal that transfer occurs. This is however not related to the self-reported use of metacognitive skills. A coding scheme was developed for annotating collaborative communication protocols. This instrument serves as a direct behavioural measure of the use of metacognitive skills. The categories are based upon the metacognitive skills derived from the literature. It appears that metacognitive behaviour does occur in the communication protocols. An analysis tool called ANALOG was developed for analysing log files of KM Quest. Initial results indicate that teams that score higher on the post-test for transfer appear to spend more time on the KM model than teams that score lower on the post-test. This suggests that perhaps the ability to transfer knowledge can be accounted for by the KM model. 1 Part of the content of this chapter is derived from the project deliverable of KITS: Christoph, L.H. and Leemkuil, H. and Ootes, W and Shostak, I.S. and Monceaux, A. (2003). Final evaluation report on the use of the final KITS learning environment prototype. Technical report D14, University of Twente . Another part was published in the form of a conference paper: Christoph, N., Anjewierden, A., Sandberg, J.A.C. & Wielinga, B.J. (2003). Measuring metacognitive skills in a collaborative gaming-simulation learning environment for the domain of knowledge management. Proceedings of EDMEDIA, Honolulu, Hawaii, pages 2313-2316. 57
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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
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 and 50: Chapter 3 KM QUEST The goal of this
Page 51 and 52: KM Quest 41 Figure 3.1. The Knowled
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: KM Quest 55 process in an electroni
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
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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
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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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