The role of metacognitive skills in learning to solve problems

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$Learning LATEX by Doing$

26 problem and the fact that students are working in small groups is appealing in the constructivist paradigm, in which the collaborative aspect is of importance as well as the use of realistic ill-defined problems. 2.4.6 Jansweijer A model for solving knowledge-rich problems was developed by Jansweijer (1988). This model is a computational model for well-defined problem solving in the Physics domain that takes into account the flexibility of human problem solvers when faced with unexpected obstacles. The specifications of the model are indicated below (Jansweijer, Elshout & Wielinga, 1990). The system contains knowledge about the concepts of the domain and their relations, and knowledge about problem solving activities. The object-problem solver is goal driven. When the object-problem solver comes to a dead-end, a meta-problem solver is invoked to resolve this impasse. The current plan of the object-problem solver is represented as a tree of goals. This is a dynamic structure that controls behaviour at the object-level. It represents the different tasks to be done. The goal tree is explicit and annotated by a trace of the objectproblem solver’s action, successes and failures. Because of this explicitness, the meta-problem solver can inspect and adjust the control structure. There is a supervising component that checks progress and detects impasses at the object-level. When they occur, the supervisor can decide to give control to the meta-problem solver for adjustment of the plan. A working memory contains all activated knowledge structures. When impasses occur at the object-level, the supervising component can give control of the problem solving process to the meta-problem solver. This meta-problem solver tries to solve the impasses of the object-problem solver in three steps. First, the difficulty of the impasse is categorised. The difficulty of the impasse is one of five possible classes: resources are wasted (too much time has been spent in a part of the problem solving process), missing information about a specific concept (information is missing to execute an action), missing declarative knowledge about a concept (definitions of
Theoretical model 27 concepts and the like), missing procedural knowledge about how to reach a goal (not knowing how to perform a task) or conflicting information (generation of contradictory information). Second, heuristic rules are applied in order to assign a class of impasses to an appropriate repair mechanism. The meta-problem solver has a repertoire of general repairs available to solve the impasse. Repairs that can always be done are: ignore the problem, start anew or give up. Conditionally applicable repairs are: 1 Gather information from an external source. 2 If you know how to do something, do it now. 3 Switch to a previous alternative of the current plan. 4 If several outcomes are possible with a procedure go with another outcome. 5 If several methods are known to reach a goal try an alternative method. 6 Try assumption. 7 Check while working backwards the outcomes of earlier procedures. Third, the repair is specified into a concrete new or adjusted plan. The repairs result in adjustments to the goal tree in the form of insertion of new specific subgoals in the control structure just before the impasse occurred or a back up to a point where there was either a choice made between methods or between different outcomes from one method. This computational model is applied to the domain of Physics, specifically thermodynamics problems, which could make it a domain-specific model. However, the functionality of the meta-problem solver is independent of the domain. The formulation of classes of impasses is generic. Assigning repairs to classes of impasses is also done in a generic way. The repairs imply that metacognitive activities are necessary in order to overcome these impasses. For instance, a learner needs to monitor comprehension of the problem, when dealing with missing declarative knowledge. When confronted with conflicting information, the plausibility of the outcomes should be checked. Jansweijer views solving a series of thermodynamics problems in the learning-by-doing paradigm (Anzai & Simon, 1979). Learning by doing can be seen as a precursor of constructivism. The main idea is that learning is invoked through activities. By trying out ideas in carefully designed activities learners can experience the results and subsequently
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 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 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 77 Figure 4.4. Scree
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Page 91 and 92:
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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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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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