The role of metacognitive skills in learning to solve problems

More documents

Recommendations

Info

$Learning LATEX by Doing$

182 Jansweijer, W., Elshout, J., and Wielinga, B. (1990). On the multiplicity of learning to solve problems. In Mandl, H., de Corte, E., Bennett, N., and Friedrich, H. F., editors, Learning and Instruction, volume 2.1, pages 127–144. Pergamon, Oxford. Jansweijer, W. N. H. (1988). PDP: An artificial Intelligence Approach to Problem Solving and Learning by Doing in a Semantically Rich Domain. PhD thesis, University of Amsterdam. Jonassen, D. (1991a). Context is everything. Educational Technology, 31(5):35–37. Jonassen, D. (1991b). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research & Development, 39:5–14. Jonassen, D. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research & Development, 45:65–95. Jonassen, D. (1999a). Computers as mindtools for schools. Prentice Hall, NJ. Jonassen, D. (1999b). Designing constructivist learning environments. In Reigheluth, C., editor, Instructional-design theories and models. Lawrence Erlbaum Associates, Mahwah, NJ. Jonassen, D. H. (1998). Designing constructivist learning environments. In Reigeluth, C., editor, Instructional theories and models. Lawrence Erlbaum Associates, Mahwah, NJ. Kerr, N. and Bruun, S. (1983). The dispensability of member effort and group motivation losses: free-rider effects. Journal of educational computing research, 5:1–15. Kline, P. (1993). A Handbook of Psychological Testing. Routledge, London. Kneser, C. and Ploetzner, R. (2001). Collaboration on the basis of complementary domain knowledge: observed dialogue structures and their relation to learning success. Learning and Instruction, 11:53–83. Kommers, P. (1992). Concepts in the world of the mind. In Kommers, P., Jonassen, D., and Mayes, J., editors, Cognitive tools for learning. Springer Verlag, Berlin. Land, S. and Hannafin, M. (1996). A conceptual framework for the development of theories-in-action with open-ended learning environments. Educational technology research & development, 44(3):37–53. Leemkuil, H., Christoph, N., de Hoog, R., de Jong, T., Ootes, S., Shostak, I., and Purbojo, R. (2002). Final specification of the instructional envelope. Technical Report D13, University of Twente. Leemkuil, H., de Jong, T., and Ootes, S. (2000). Review of educational use of games and simulations. Technical Report D1, University of Twente. Leemkuil, H., de Jong, T., Ootes, S., Shostak, I., Purbojo, R., de Hoog, R., and Kruizinga, E. (2001). Initial specification of the instructional envelope. Technical Report D5, University of Twente. Leemkuil, H., Jong, T. D., Hoog, R. D., and Christoph, N. (2003). Km quest: A collaborative internet-based simulation game. Simulation & Gaming, 34(1):89–111. Masui, C. and de Corte, E. (1999). Enhancing learning and problem solving skills: orienting and self-judging, two powerful and trainable learning tools. Learning and instruction, 9:517–542. Mayer, R. (2002). Rote versus meaningful learning. Theory into practice, 41(4):226– 233. Mettes, C. and Pilot, A. (1980). Over het leren oplossen van natuurwetenschappelijke problemen [On learning to solve science problems]. PhD thesis, Technische Hogeschool Twente. Mitchel, A. and Savill-Smith, C. (2004). The use of computer and video games for learning. Technical report, Learning and skills development agency.
REFERENCES 183 Nelson, T. (1999). Cognition versus metacognition. In Sternberg, R., editor, The nature of cognition, pages 625–641. MIT Press, Cambridge, MA. Nelson, T. and Narens, L. (1990). Metamemory: a theoretical framework and new findings. The psychology of learning and motivation, 26:125–173. Newell, A. and Simon, H. (1972). Human problem solving. Prentice Hall, Englewood Cliffs, NJ. Oates, J., Gove, J., Goudge, A., Hill, R., Littleton, K., Christoph, L., Edwards, N., Gardner, R., Grayson, A., and Manners, P. (2000). focus - a cd-rom based application for developing observation skills. In Paper presented at the SURF Conference, Rotterdam, The Netherlands. Osman, M. and Hannafin, M. (1992). Metacognition research and theory: Analysis and implications for instructional design. Educational Technology Research & Development, 40(2):83–99. Perkins, D. (1992). Technology meets constructivism: do they make a marriage? In Duffy, T. and Jonassen, D., editors, Constructivism and the technology of instruction: a conversation. Lawrence Erlbaum Associates, Hillsdale, NJ. Pintrich, P. and de Groot, E. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, 82:33–40. Pintrich, P., Smith, D., Garcia, T., and McKeachie, W. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (mslq). Educational and psychological measurement, 53:801–813. Pintrich, P., Wolters, C., and Baxter, G. (2000). Assessing metacognition and selfregulated learning. In Schraw, G. and Impara, J., editors, Issues in the measurement of metacognition, pages 43–146. Buros Institute of Mental Measurements, Lincoln, NE. Pressley, M. and Afflerbach, P. (1995). Verbal protocols of reading: The nature of constructively responsive reading. Erlbaum, Mahwah, NJ. Prins, F. (2002). Search and see: the roles of metacognitive skillfulness and intellectual ability during novice inductive learning in a complex computer-simulated environment. PhD thesis, University of Leiden. Randel, J., Morris, B., Wetzel, C., and Whitehill, B. (1992). The effectiveness of games for educational purposes: a review of recent research. Simulation and gaming, 23(3):261–276. Saab, N. (2005). Chat and explore. The role of support and motivation in collaborative scientific discovery learning. PhD thesis, University of Amsterdam. Salomon, D. and Perkins, D. (1989). Rocky roads to transfer: rethinking mechanisms of a neglected phenomenon. Educational psychologist, 24(2):111–142. Salomon, G. (1993). No distribution without individuals cognition: A dynamic interactional view. In Salomon, G., editor, Distributed cognitions. Psychological and educational considerations, pages 111–138. Cambridge University Press, Cambridge, UK. Sandberg, J. and Andriessen, J. (1997). Where is AI and how about education? In du Boulay, B. and Mizoguchi, R., editors, Artificial Intelligence in Education: Knowledge and media in Learning Systems, pages 545–552. Amsterdam University Press, Amsterdam. Sandberg, J. and van Joolingen, W. (submitted). Constructivist principles in technology rich environments: a framework for analysis and design. Instructional Science. Schoen, D. (1988). Educating the reflective practitioner. Jossey-Bass publishers, San Francisco.
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 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
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
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 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
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: REFERENCES 181 Duffy, T. and Cunnin
Page 195: REFERENCES 185 van Harmelen, F., Wi
Page 198 and 199: Prototyping of CMS Storage Manageme
Page 200 and 201: Human-Computer Interaction and Pres
Page 202: A Model-driven Approach for Buildin
show all

The role of metacognitive skills in learning to solve problems

Create successful ePaper yourself

Delete template?

Save as template?