The role of metacognitive skills in learning to solve problems

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94 The score on KMQUESTions indicates the proportion of correct answers. Although the questions in KMQUESTions concern multiplechoice items with four alternatives, no correction for guessing is applied. In general, in the educational literature it is common sense to use the percentage or proportion of correctly answered items as the score. Only in order to decide on the cutoff between students who pass and do not pass the test, the correction for guessing is recommended. In table 5.2 the means and standard deviations of the scores on KMQUESTions preand post-test for group AB and BA and overall are presented. In order to check whether both versions are equally difficult, the pretest scores of both groups are compared. No significant differences exist concerning pre-test scores of both types of knowledge for the different test versions. However, group BA achieves significantly lower post-test scores than group AB concerning procedural knowledge (T = 2.91, p < 0.01). Both groups do not differ concerning declarative knowledge (T = 1.80, p = 0.08). This means that differences between both groups are due to the ability of the participants, meaning that group BA is weaker than group AB. A paired samples t-test was used in order to compare the sample means on pre- and post-test. This parametric test is used when test scores are distributed normally or when the total number of cases (per condition) is 30 or more. A large increase in overall test scores exists from pre- to post-test, which is significant (T = -13.57, p < 0.01). The differences between the pre- and post-test measurement for declarative knowledge are significant (T = -5.04, p < 0.01) as well as for procedural knowledge (T = -5.77, p < 0.01). The hypothesis that students learn from playing KM Quest can be accepted. No differences exist between declarative and procedural knowledge in the pre-test, nor between these parts in the post-test. This means that students acquire both declarative and procedural knowledge to the same extent. In the previous chapter it was shown that no significant correlations between game results and transfer could be established. This was possibly due to the rather low number of participants in the subsample. In this study, the relation between game results and meaningful learning is checked. No significant correlations exists between post-test measurements of declarative and procedural knowledge and game results. Students differ in the time they spend playing KM Quest. Figure 5.2 shows a box plot of the results. The upper and lower side of the rectangle represent the first and third quartile (or respectively the 25th and 75th percentile). Together they represent the interquartile range or box length. Fifty percent of the data falls within the interquartile range. The whiskers above and below the rectangle represent respectively the
Study II: learning revisited 95 highest and lowest score within 1.5 interquartile range. The black line indicates the median. Two outliers (score within 1.5 to 3 box length) and one extreme case (score further away than 3 times the box length or interquartile range) concerning time spent playing KM Quest existed. These were deleted from the data. The descriptive statistics of time-ontask without these three cases are given in table 5.3. Figure 5.2. Box plot of time-on-task in hours for all cases. Mean SD min max N Time-on-task 05:08:24 02:19:12 01:48:36 12:12:00 28 Table 5.3. Descriptive statistics for time-on-task measured in hours, minutes and seconds without outliers and extreme cases over individuals. One assumption is that time-on-task is positively related to learning success. This is the case as post-test scores on declarative knowledge are significantly positively related to time-on-task (r = 0.54, p < 0.01, N = 28 ) and so is procedural knowledge (r = 0.46, p < 0.05, N = 28). Time-on-task explains between 21% and 29% of the variance of the post-test scores. In order to explore this further, a within-subjects analysis of variance with time-on-task as covariate was carried out. This analysis can be used when repeated measures of participants have been collected (within-subject variables). Since no between-subject variables were included, no independent groups were distinguished. A covariate
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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
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
Page 100 and 101: 90 different occasions or settings.
Page 102 and 103: 92 conceptual correctness (54) the
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 142 and 143: 132 Cog Tas Meta Total No-model con
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
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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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