A model-based approach for assessment and motivation

Computer Science and Information Systems

Volumn 9, Issue 2, 2012, Pages 893-915

  Michael Spector, J ^a   Kim, Chanmin ^b  

^a UNIVERSITY OF NORTH TEXAS   (United States)

^b UNIVERSITY OF GEORGIA   (United States)

Author keywords

Assessment; Complex problem solving; Critcal reasoning; Mental model; Model facilitated learning; Motivation; Problem conceptualization; Problem representation

Indexed keywords

EID: 84865208073     PISSN: 18200214     EISSN: None     Source Type: Journal    
DOI: 10.2298/CSIS111226016S     Document Type: Article

References (112)

1. Al-Diban, S., & Seel, N. M. (1999). Evaluation als Forschungsaufgabe von Instruktionsdesign - Dargestellt am Beispiel eines multimedialen Lehrprogramms. Unterrichtswissenschaft, 27 (1), 29-60
2. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
3. Anzai, Y., & Yokoyama, T. (1984). Internal models in physics problem solving. Cognition and Instruction, 1, 397-450.
4. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge, UK: Cambridge University Press.
5. Bhatta, S. R., & Goel, A. (1997). Learning generic mechanisms for innovative strategies in adaptive design. The Journal of the Learning Sciences, 6 (4), 367-396.
6. Braine, M. D. S. (1990). The natural logic approach to reasoning. In W.Overton (Ed.), Reasoning, necessity, and logic: Developmental perspectives (pp. 133-157). Hillsdale, NJ. Lawrence Erlbaum Assoc.
7. Braine, M. D. S., & O'Brien, D. P. (Eds.) (1998). Mental logic. Mahwah, NJ: Lawrence Erlbaum Assoc.
8. Briggs, P. (1990). The role of the user model in learning as an internally and externally directed activity. In D. Ackermann, & M.J. Tauber (Eds.), Mental models and human-computer interaction 1 (pp. 195-208). Amsterdam: Elsevier Publ.
9. Brown, D. E., & Clement, J. (1989). Overcoming misconceptions via analogical reasoning: abstract transfer versus explanatory model construction. Instructional Science, 18, 237-261.
10. Bruner, J. S. (1966). Toward a theory of instruction. Cambridge, MA: Harvard University Press.
11. Buckley, B. C., & Boulter, C. J. (1999). Analysis of representation in model-based teaching and learning in science. In R. Paton & I. Neilsen (Eds.), Visual representations and interpretations. (pp. 289-294). London: Springer.
12. Carlson, H. L. (1991). Learning style and program design in interactive multimedia. Educational Technology Research and Development, 39 (3), 41-48.
13. Casey, C. (1996). Incorporating cognitive apprenticeship in multi-media. Educational Technology Research and Development, 44 (1), 71-84.
14. Cheng, P. W., & Holyoak, K.J. (1985). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391-426.
15. Collins, A., Brown, J.S. & Newman, S.E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L.B. Resnick (Ed.) (1989), Knowing, learning, and instruction (pp. 453-494). Hillsdale, NJ: Erlbaum.
16. Cornelissen, J. P. (2006). Making Sense of Theory Construction: Metaphor and Disciplined Imagination Organization Studies, 27, 1579-1597.
17. Darabi, A.A., Nelson, D.W., & Seel, N.M. (i.pr.). A dynamic mental model approach to examine schema development of a troubleshooting skill: Delayed retention of mental models as a test of schema development. Technology, Instruction, Cognition, and Learning.
18. Dewey, J. (1910). How we think. New York: D. C. Heath.
19. Doerr, H.M. (1996). Integrating the study of trigonometry, vectors, and force through modeling. School Science and Mathematics, 96 (8), 407-418.
20. Dole, J.A., & Sinatra, G.M. (1998). Reconceptualizing change in the cognitive construction of knowledge. Educational Psychologist, 33 (2/3), 109-128.
21. Dörner, D. (1976). Problemlösen als Informationsverarbeitung. Stuttgart: Kohlhammer.
22. Dörner, D., & Wearing, A. (1995). Complex problem solving: Toward a (computer-simulated) theory. In P. A. Frensch & J. Funke (Eds.), Complex problem solving: The European Perspective (pp. 65-99). Hillsdale, NJ: Lawrence Erlbaum Associates.
23. Evans, J.St.B.T. (1982). The psychology of deductive reasoning. London: Routledge.
24. Farnham-Diggory, S. (1972). Cognitive processes in education: A psychological preparation for teaching and curriculum development. New York: Harper & Row.
25. Friedman, M. (2002). Kuhn, and the rationality of scienc. Philosophy of Science, 69, 171-190.
26. Funke, J. & Frensch, P. A. (1995). Complex problem solving research in North America and Europe: An integrative review. Foreign Psychology, 5, 42-47.
27. Funke, J. (1991). Solving complex problems: Exploration and control of complex systems. In R.J. Sternberg & P.A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp.185-222). Hillsdale, NJ: Lawrence Erlbaum.
28. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155-170.
29. Gibbons, A.S. (2001). Model-centered instruction. Journal of Structural Learning and Intelligent Systems, 14 (4), 511-540.
30. Gibbons, A.S. (2003). Model-centered learning and instruction: Comments on Seel (2003). Technology, Instruction, Cognition and Learning, 1 (3), 291-299.
31. Glaser, R., Lesgold, A., & Lajoie, S. (1987). Toward a cognitive theory for the measurement of achievement. In R.R. Ronning, J. Glover, J.C. Conoley, & J.C. Witt (Eds.), The influence of cognitive psychology on testing and measurement (pp. 41-85). Hillsdale, NJ: Lawrence Erlbaum.
32. Gravemeijer, K., Cobb, P., Bowers, J., & Whitenack, J. (2000). Symbolizing, modeling, and instructional design. In P. Cobb, E. Yackel & K. McClain (Eds.), Symbolizing and communicating in mathematics classrooms. Perspectices on discourse, tools, and instructional design (pp. 225-273). Mahwah, NJ: Erlbaum.
33. Greeno, J. G. (1989). Situations, mental models, and generative knowledge. In D. Klahr & K. Kotovsky (Eds.), Complex information processing (pp. 285-318). Hillsdale, NJ: Lawrence Erlbaum.
34. Haberlant, K. (1999), Human memory., Boston: Allyn & Bacon.
35. Hale, C.R., & Barsalou, L.W. (1995). Explanation content and construction during system learning and troubleshooting. The Journal of the Learning Sciences, 4 (4), 385-436.
36. Hambrick, D., & Engle, R. (2003). The role of working memory in problem solving. In J. Davidson & R. Sternberg (Eds.), The psychology of problem solving (pp. 176-206). NY: Cambridge University Press.
37. Hammer, D. (1997). Discovery learning and discovery teaching. Cognition and Instruction, 15 (4), 485-529.
38. Heidegger, M. (1968). What is called thinking? [Tr. J. G. Gray]. New York: Harper & Row.
39. Hoffman, B., & Schraw, G. (2010). Conceptions of efficiency: Applications in learning and problem solving. Educational Psychologist, 45, 1-10.
40. Hofstadter, D., & Sander, E. (2010). The essence of thought, New York: Basic Books.
41. Ifenthaler, D., & Seel, N.M. (2005). The measurement of change: Learning-dependent progression of mental models. Technology, Instruction, Cognition, and Learning, 2 (4), 321-340.
42. Jacobson, M.J. (2000). Problem solving about complex Systems: differences between experts and novices. In B. Fishman & S. O'Connor-Divelbiss (Eds.), Fourth International Conference of the Learning Sciences (pp. 14-21). Mahwah, NJ: Erlbaum.
43. Johnson-Laird, P.N. (1983). Mental models. Towards a cognitive science of language, inference, and consciousness. Cambridge: Cambridge University Press.
44. Johnson-Laird, P.N. (1989). Mental models. In M.I. Posner (Ed.), Foundations of cognitive science (pp. 469-499). Cambridge, MA:The MIT Press.
45. Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide. San Francisco, CA: Jossey-Bass.
46. Jonassen, D. H. (2006). Modeling with technology: Mindtools for conceptual change. Columbus, OH: Merrill/Prentice-Hall.
47. Jonassen, D. H. (2007). Learning to solve complex, scientific problems. Mahway, NJ: Erlbaum.
48. Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York: Routledge.
49. Jonassen, D. H., & Kim, B. (2010). Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology Research & Development, 58(4), 439-457.
50. Kafai, Y.B., & Ching, C.C. (2004). Children as instructional designers: Apprenticing, questioning, and evaluating ine the Learning Science by Design project. In N.M. Seel & S. Dijkstra, S. (Eds.). Curriculum, Plans and Processes of Instructional Design: International Perspectives (pp. 115-130). Mahwah, NJ: Lawrence Erlbaum.
51. Kant, I. (1781, 2009). The critique of pure reason [Tr. J. M. D. Meiklejohn]. Adelaide, Australia: The University of Adelaide eBooks.
52. Karplus, R. (1969). Introductory physics: A model approach. New York: Benjamins.
53. Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. New York: Springer.
54. Kim, C. (2012a). Beliefs about learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp.450-452). Dordrecht: Springer.
55. Kim, C. (2012b. Virtual change agents. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 3405-3407). Dordrecht: Springer.
56. Kim, C. (2012c). Motivational variables in learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (2347-2348). Dordrecht: Springer.
57. Kim C. (in press). The role of affective and motivational factors in designing personalized learning environments. Eduational Technology Research & Development.
58. Kim, C., & Balaam, M. (2011). Monitoring affective and motivational aspects of learning experience with the Subtle Stone. Proceedings of the 11th IEEE International Conference on Advanced Learning Technologies (ICALT) (pp.640-641). Athens, GA: IEEE Computer Society Publications.
59. Kim, C., Keller, J. M., & Baylor, A. L. (2007, December). Effects of motivational and volitional messages on attitudes toward engineering: Comparing text messages with animated messages delivered by a pedagogical agent. Proceedings of the IADIS International Conference CELDA, Algarve, Portugal.
60. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, and problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75-86.
61. Kluwe, R.H., & Haider, H. (1990). Modelle zur internen Repräsentation komplexer technischer Systeme. Sprache & Kognition, 9 (4), 173-192.
62. Kolodner, J.L., Camp, P.J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., & Ryan, M. (2004). Promoting deep science learning through case-based reasoning: Rituals and practices in Learning by Design classrooms. In N.M. Seel & S. Dijkstra, S. (Eds.). Curriculum, Plans and Processes of Instructional Design: International Perspectives (pp. 89-114). Mahwah, NJ: Lawrence Erlbaum.
63. Krems, J. F. (1995). Cognitive flexibility and complex problem solving. In P.A. Frensch & J. Funke (Eds.), Complex problem solving. The European perspective (pp. 201-218). Hillsdale, NJ: Lawrence Erlbaum.
64. Kurtz, K. J., Miao, C. H., & Gentner, D. (2001). Learning by analogical bootstrapping. Journal of the Learning Sciences, 10, 417-446.
65. Lakoff, G., & M. Johnson (1980). Metaphors we live by. Chicago: University of Chicago Press.
66. Lakoff, G., and M. Johnson (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought. New York: Basic Books.
67. Lave, J., & Wenger, E. (1990). Situated learning: Legitimate peripheral participation, Cambridge, UK: Cambridge University Press.
68. Lesh, R., & Doerr, H.M. (Eds.) (2003). Beyond constructivism. Models and modelling perspectives on mathematics problem solving, learning, and teaching. Mahwah, NJ: Lawrence Erlbaum.
69. Markman, A.B. (1998). Knowledge representation. Mahwah, NJ: Erlbaum.
70. Mayer, R. E., & Wittrock, M. C. (2006). Problem solving. In P. A. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287-304). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
71. Mayer, R. E., Mathias, A., & Wetzell, K. (2002). Fostering understanding of multimedia messages through pre-training: Evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied, 8, 147-154.
72. Mayer, R.E. (1989). Models for understanding. Review of Educational Research, 59 (1), 43-64.
73. Mayer, R.E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59 (1), 14-19.
74. Merrill, M. D, (2002). First principles of instructional design. Educational Technology Research & Development, 50(3), 43-59.
75. Milrad, M., Spector, J. M., & Davidsen, P. I. (2003). Model Facilitated Learning. In S. Naidu (Ed.), Learning and teaching with technology: Principles and practices (pp. 13-27). London: Kogan Page.
76. Motah, M. (2006), The Ontogeny of Memory and Learning. In Proceedings of 2006 IS Conference. p.p 221-231.
77. Motah, M. (2007), Learning, Types of Learning, Traditional Learning Styles. In Proceedings of the 2007 CSITEd Conference.
78. Nair, K.U., & Ramnarayan, S. (2000). Individual differences in need for cognition and complex problem solving. Journal of Research in Personality, 34 (3), 305-328.
79. Neubauer, A. C. & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33, 1004-1023.
80. Norman, D.A. (1983). Some observations on mental models. In D. Gentner & A.L. Stevens (Eds.), Mental models (pp. 7-14). Hillsdale, NJ: Erlbaum.
81. Oaksford, M., & Chater, N. (1996). Rational explanation of the selection task. Psychological Review, 103 (2), 381-391.
82. Penner, D. E. (2001). Cognition, computers, and synthetic science: Building knowledge and meaning through modeling. Review of Research in Education, 25, 1-35.
83. Piaget, J. (1951). The psychology of intelligence. London: Routledge and Kegan Paul.
84. Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research & Development, 58(1), 3-18.
85. Quine, W. V. O., & Ullian, J. S. (1978). The web of belief (2nd ed.). New York, NY: Random House.
86. Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. The Journal of Learning Sciences, 7(2), 153-172.
87. Rips, L. J. (1984). Reasoning as a central ability. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 2, pp. 105-147). Hillsdale, NJ. Erlbaum.
88. Rumelhart, D. D. (1980). Schemata: The building bloack of cognition. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 33-58). Hillsdale, NJ: Erlbaum.
89. Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart, & The PDP research group (Eds.), Parallel distributed processing. Explorations in the microstructure of cognition. Volume 2: Psychological and biological models (pp. 7-57). Cambridge, MA: MIT Press.
90. Schank, R. C., & Abelson, R. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Earlbaum Assoc.
91. Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32 (1), 102-119.
92. Schraw, G. (2006). Knowledge: Structures and processes. In P. A. Alexander & P. Winne (Eds.), Handbook of Educational Psychology (2nd ed.), (pp. 245-264). Mahwah, NJ: Erlbaum.
93. Seel, N. M. (1999). Educational diagnosis of mental models: Assessment problems and technology-based solutions. Journal of Structural Learning and Intelligent Systems, 14 (2), 153-185.
94. Seel, N. M. (2003). Model-centered Learning and Instruction. Technology, Instruction, Cognition, and Learning, 1 (1), 59-85.
95. Seel, N. M. (2005). Designing Model-Centered Learning Environments: Hocus-pocus - Or the Focus Must Strictly Be on Locus. In J.M. Spector, C. Ohrazda, A. Van Schaak & D.A. Wiley (Eds.), Innovations in Instructional Technology: Essays in Honor of M. David Merrill (pp. 65-90). Mahwah, NJ: Lawrence Erlbaum.
96. Seel, N. M., Al-Diban, S., & Blumschein, P. (2000). Mental models and instructional planning. In M. Spector & T.M. Anderson (Eds.), Integrated and holistic perspectives on learning, instruction and technology: Understanding complexity (pp. 129-158). Dordrecht, NL: Kluwer Academic Publishers.
97. Spector, J. M. (2001). A philosophy of instructional design for the 21st century? Journal of Structural learning and Intelligent Systems, 14(4), 307-318.
98. Spector, J. M. (2010). Mental representations and their analysis: An epistemological perspective. In D. Ifenthaler, P. Pirnay-dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (17-40). New York, NY: Springer.
99. Spector, J. M. (2012). Naturalistic epistemology. In N. M. Seel (Ed.) The encyclopedia of the sciences of learning (pp. 2433-2435). New York. Springer.
100. Spector, J. M., & Koszalka, T. A. (2004). The DEEP methodology for assessing learning in complex domains (Final report to the National Science Foundation Evaluative Research and Evaluation Capacity Building). Syracuse, NY: Syracuse University.
101. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In V. Patel (Ed.), Proceedings of the 10th Annual Conference of the Cognitive Science Society (pp. 375-383). Mahwah, NJ: Erlbaum.
102. Stachowiak, H. (1973). Allgemeine Modelltheorie. Wien: Springer.
103. Sterman, J. D. (1994). Learning in and about complex systems. Systems Dynamics Review 10, (2-3), 291-330.
104. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285.
105. Turner, R.M. (1994). Adaptive reasoning for real-world problems: A schema-based approach. Hillsdale, NJ: Lawrence Erlbaum Associates.
106. van Merriënboer, J. J. G., & Kirschner P. (2007). Ten steps to complex learning: A systematic approach to four-component instructional design theory. New York: Routledge.
107. Volet, S. E. (1991). Modelling and coaching of relevant, metacognitive strategies for enhancing university students ́ learning. Learning and Instruction, 1 (4), 319-336.
108. Wertheimer, M. (1959). Productive thinking (Enlarged ed.). New York: Harper & Row.
109. White, B. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10 (1), 1-100.
110. Wittgenstein, L. (1922). Tractatus logico-philosophicus (translated by C. K. Ogden). London, UK: Routledge & Kegan Paul.
111. Wittgenstein, L. (1953). Philosophical investigations (translated by G. E. M. Anscombe). Oxford, UK: Blackwell.
112. Young, R. (1983). Surrogates and mappings: Two kinds of conceptual models for interactive devices. In D. Gentner & A.L. Stevens (Eds.), Mental models (pp. 35-52). Hillsdale, NJ: Erlbaum.