The role of metacognitive skills in learning to solve problems

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68 Pre-test Post-test Mean SD N Mean SD N Group AB 3.36 0.98 14 3.67 2.04 12 Group BA 3.47 1.19 13 4.02 1.86 13 Overall 3.41 1.07 27 3.84 1.92 25 Grade - - - 6.91 1.0 27 Table 4.2. Means and standard deviations of the pre- and post-test parallel versions of the HollandSky test (groups AB and BA) and grade, all measured on a ten-point scale (1-10). are found between the pre- and post-test (z = -0.90, N - Ties = 25, p = 0.18 one-tailed). The implication is that KM Quest does not seem to enhance transfer. Careful screening of the test results reveals however that much data were missing. Respondents left many questions blank, specifically in the post-test. In general, when more than 5 percent of the data is missing, a missing data analysis should be performed. This can be done to check if dropping the cases with missing values produces a bias in the resulting data set. As for the pre-test, 27 respondents each answered 11 question items resulting in 297 data points. In total, 12 items were left blank resulting in 4% missing data. For the post-test, 25 respondents each answered 11 items. This results in 275 data points of which 40 were left blank. This is over 14% of missing data so further investigation is needed. If these missing data had been randomly distributed, values could have been filled in based on estimates. However, in this case it turns out that the missing values are not randomly distributed as many respondents left the last three items unanswered and some respondents skipped the remaining test items after having completed only the first few items. Missing data do not differ over test versions. Possible reasons for the high percentage of missing data in the posttest are that respondents are not fully motivated to give appropriate answers to the question items because it is not part of the institutional grade and the open test format takes a lot of time to fill in. Unfortunately, the different test versions could not overcome these motivational effects. For the calculation of the initial results, as presented in table 4.2, missing data were treated as wrong answers. Blank question items were given zero points instead of deleting cases. This might be the reason why the mean scores of the pre- and post-test do not differ from each
Study I: Pilot 69 other. It also explains the relatively high reliability of both test versions with only a small number of items. It was decided then to exclude all the cases with missing data from the sample. As a result, teams are incomplete and two teams dropped out entirely (namely team 84 and 87). Fourteen cases remain (pre-test: mean = 3.80, SD = 0.75; post-test: mean = 4.84, SD = 1.70). Again the Wilcoxon signed-ranks test was used to compare scores on pre- and post-test. This time, a significant difference exists which indicates a learning effect (z = -2.17, N - Ties = 14, p < 0.05, one-tailed). This is evidence that transfer occurs. These participants are able to transfer their knowledge and skills learned in KM Quest to a different case. For the remainder of this chapter, the results of this subsample (N = 14) are discussed, unless specifically stated otherwise. An alternative measure for learning is the weighted game result. In the starting situation in quarter 1, the weighted average of the organisational effectiveness indicators is 7. Each team enters the game under the same start conditions. However, the system fires the events randomly which means that each event has more or less the same chance to occur. There is no indication that events differ in degree of difficulty. The average game result in quarter 7 of the subsample is 7.15 (SD = 0.67). This is hardly better than the starting situation (see also table 4.3). However, all teams stay well above the decay function of KM Quest. Figure 4.1, however, shows that teams vary a great deal in their game results over quarters. The team with the highest game score (79) finishes with a game result of 7.99, the team with the lowest game score (80) finishes with a game result of 6.09. Q1 Q2 Q3 Q4 Q5 Q6 Q7 Mean 7.00 6.95 7.16 7.31 7.06 7.05 7.15 SD 0.00 0.28 0.43 0.52 0.71 0.82 0.67 Table 4.3. Means and standard deviations of game results of the teams for seven quarters, measured on a ten-point scale (1-10)(N = 14). The other alternative measure for learning in the field of KM is the institutional course grade, which consisted of a combined measure for writing essays, carrying out a case study supported by lectures, books and articles. The grade did not include any knowledge and skills concerning KM Quest; it was independent of the game. The average grade of the subsample is 6.87 (SD = 1.05).
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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
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 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: Study I: Pilot 67 objective, a seco
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
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
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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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