The role of metacognitive skills in learning to solve problems

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132 Cog Tas Meta Total No-model condition GROUP 9.32 1.80 10.60 21.72 TOOL 0.97 0.26 0.18 1.41 KM 16.14 2.39 8.35 26.87 Total 26.42 4.45 19.13 50.00 Model condition GROUP 9.20 2.59 14.85 26.64 TOOL 0.84 0.40 0.43 1.67 KM 11.39 2.08 8.22 21.69 Total 21.43 5.07 23.50 50.00 Total 47.85 9.52 42.63 100.00 Table 6.18. Percentage of contributions over several categories of the coding scheme in both conditions (Bold indicates a significant difference). condition also score significantly higher on G META (T = -2.52, p < 0.05) than students in the no-model condition. Students in the no-model condition score significantly higher on KM COG (T = 2.58, p < 0.01) than students in the model condition. These results indicate that the group process in learning, for example, asking approval or asking questions to other team members, is more present in the model condition than in the no-model condition. Also, it appears that in the model condition, students make more metacognitive contributions, compared to students in the no-model condition who make more cognitive remarks. In order to explore the data further, both conditions were compared regarding the use of specific metacognitive skills. It turns out that students in the model condition make more contributions in the following categories: G PLAN (T = -6.32, p < 0.01) and G MONG (T = -4.14, p < 0.05). No other significant differences exist. Typical contributions in G PLAN are “And now, people?” or “Let’s continue?” and “Please tell me, what shall we do?”. Typical contributions in G MONG are “Something like this?”(asking whether one agrees upon the content of a step in the KM model), “Yes, I have read this” (in response to a direct question concerning a contribution to a KM model step) and “Dave, do you agree with this?” (again about agreeing on the content of a KM model step). These data indicate that students in the model condition make more contributions that focus on the regulation of the task model in contrast to content (domain) regulation. The fact that students in the model condition score higher on G TASK, G PLAN and G MONG supports this explanation. These students were instructed to complete all steps of
Study III: added value of the task model 133 the KM model. The learning environment possibly requires a different type of regulation in the two conditions. This is explored further in the next two sections. 6.3.3.2 Effects of condition and metacognition The General Linear Model (GLM) repeated measures procedure was used in order to test the hypothesized interaction effect between condition and use of metacognitive skills (hypotheses 3 and 4). This is the same procedure as was used for testing the hypothesized interaction effect concerning the self-report measures of metacognition (see section 6.3.2.2). Independent variables were condition and the concurrent measures of metacognition (G META and KM META). The metacognitive contributions related to the TOOL category were discarded since they did not occur frequently. Dependent variables were the preand post-test measurements for declarative, general and KM modelspecific procedural knowledge. A MANOVA was used for analysis of the transfer scores. Only within-subject effects or between-subject effects that include metacognition are reported since the other effects have already been discussed at length in the earlier sections of this chapter. No within-subject interaction effects exist nor between-subject interaction effects. Since the latter are hypothesized, table 6.19 gives the actual F and p-values. These results indicate that with regard to the concurrent measure of metacognition no interaction effect between condition and metacognition exists. The data do not support hypotheses 3 and 4. One main effect of KM META can be reported though. Students in the KM META+ group score higher on specific procedural knowledge than students in the KM META− group (F = 4.06, p = 0.05). Students in the KM META− group score initially 0.46 (SD = 0.15) on KM modelspecific procedural knowledge in the pre-test. In the post-test they score 0.61 (SD = 0.13). Students in the KM META+ group initially score 0.50 (SD = 0.18) on the pre-test and on the post-test they score 0.65 (SD = 0.14). No effects of G META exist. 6.3.3.3 Correlation between learning and all measures of metacognition A partial correlation analysis was performed in order to investigate the relation between the prospective, concurrent and retrospective measures of metacognition and the results on the knowledge tests (post-test) for the individual conditions. Pre-test scores of learning measures were partialed out. This method gives additional insight into the data with
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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
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Study I: Pilot 61 Also, the questio
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Study I: Pilot 63 however, to what
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Study I: Pilot 65 ceived 12 questio
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Study I: Pilot 67 objective, a seco
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Study I: Pilot 69 other. It also ex
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Study I: Pilot 71 In conclusion, th
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Study I: Pilot 73 Finally, the othe
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Study I: Pilot 75 Figure 4.2. Scree
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Page 91 and 92: Study I: Pilot 81 Figure 4.7. 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 122 and 123: 112 T-PROS because they did not occ
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 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
Page 174 and 175: 164 to learning to solve problems.
Page 176 and 177: 166 cognition in a constructivist l
Page 178 and 179: 168 forward. The most important one
Page 181 and 182: Chapter 9 SAMENVATTING Het huidige
Page 183 and 184: Samenvatting 173 De aanwezigheid va
Page 185 and 186: Samenvatting 175 gebruik van metaco
Page 187 and 188: Samenvatting 177 nificant leereffec
Page 189 and 190: References Akhras, F. and Self, J.
Page 191 and 192: 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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