The role of metacognitive skills in learning to solve problems

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32 Second, the meta-level in problem solving can be replaced by a tasklevel. This level contains task knowledge about particular types of tasks. It also contains methods that describe how to solve particular types of tasks. It controls problem solving at the object-level. Having a task-level available converges with the notion of compiled task models discussed in the models of problem solving in this chapter. Mettes and Pilot, Hamel and Erkens identify compiled task models that consist of orientation, execution (or solution) and evaluation tasks. These tasks are decomposed, depending on the type of task that it addresses, in subtasks and activities. The activities are executed at the object-level. The existence of a task model is useful for solving well-defined problems (Mettes & Pilot, 1980). For solving ill-defined problems the necessity of such a model is even greater since the problem space is much larger. At the same time though, it is more difficult to develop such a model. Third, the models discussed in this chapter only pay limited attention to learning, especially when learning is seen in the perspective of constructivism. The models that do take learning into account differ in the extent to which they fit within constructivism. None of the models incorporate all constructivist learning principles. The majority of the models focuses on individual learning instead of collaborative learning. The models are quite limited in describing how learning to solve realistic, ill-defined problems in the constructivist paradigm could be supported and how this possibly differs from solving well-defined problems. Only the model by Jansweijer identifies the need to learn through meaningful experiences in the learning-by-doing paradigm. Finally, the need for self-regulation and reflection is acknowledged in several models, mostly from an AI perspective. 2.5 Theoretical model for learning to solve problems Based on the discussion of the models of problem solving and the conclusions above, in this section a theoretical model of learning to solve problems is presented. The theoretical model is unfolded in two ways. First, the different levels of the model are discussed. This is depicted in figure 2.8. Second, learning to solve problems in this model is explained. This is indicated in figure 2.9. 2.5.1 Levels in problem solving In the theoretical model, three levels in the problem solving process are distinguished. These are the object-, task- and the meta-level. Distinguishing three instead of only two levels (meta- and object-level) in
Theoretical model 33 Source Domain Level Problem Newell & Simon, 1972 Generic Object-level: applying sequences of operators to states in order to achieve a goal Welldefined Mettes & Pilot, 1980 Task-level: compiled task model that focuses on: representing the problem internally (orientation), choosing standard methods to solve the problem (solution) and testing the solution (evaluation) Thermodynamics Wellstructured Hamel, 1990 Architecture Task-level: compiled task model that comprises analysis, synthesis and moulding schemas in which orientation, execution and evaluation activities take place Erkens, 1997 Logic Task-level: compiled task model that lies at the heart of the functionality of the Problem Solver component Welldefined Welldefined Schoenfeld, 1985; 1987; 1992 Mathematics Meta-level: applying selfregulatory skills and belief systems Illdefined Jansweijer, 1988 Thermodynamics Meta-level: meta-problem solver detects impasses of the objectproblem solver and it has a repertoire of (domain-independent) repairs Welldefined Van Harmelen et al., 1992 Generic Meta-level: generic meta-system supervises the functional behaviour of the object-system and it models the object-system Self, 1994 Generic Meta-level: meta-system contains a representation of the object-system it reasons about Welldefined Illdefined Table 2.1. Overview of the characteristics of the problem solving models.
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 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 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
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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
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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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