Abstract
This article investigates a complex Aptitude Treatment Interaction (ATI), of intelligence and metacognitive skill as aptitudes with structuredness of learning environment as treatment. A more structured learning environment is usually regarded as beneficial to learning in low intelligence students, whereas it may not affect or may even interfere with learning in high intelligence students. The overall analyses of four studies are presented, including a total of 99 subjects. High and low intelligence novices passed through either structured or unstructured simulation environments in the domains of heat theory, electricity, or statistics. Thinking-aloud protocols were analyzed in order to assess the metacognitive skillfulness of subjects. Several learning tests were administered, assessing both declarative and procedural domain knowledge. The results show that structuredness of learning environment did not affect learning in high intelligence subjects, irrespective of their level of metacognitive skillfulness. However, the structured learning environment yielded enhanced learning performances in low intelligence subjects with a low level of metacognitive skillfulness, while it interfered with learning in low intelligence subjects with a relatively high level of metacognitive skillfulness.
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References
Brown, A. L. (1978). Knowing when, where, and how to remember a problem of metacognition, in R. Glaser, ed.,Advances in Instructional Psychology, Vol. 1 (pp. 77–165). Hillsdale, NJ: Erlbaum.
Bruner, J. S. (1976).The Relevance of Education. Harmondsworth: Penguin Education.
Collins, A. & Stevens, A. L. (1983). A cognitive theory of inquiry teaching, in C. M. Reigeluth, ed.,Instructional Design Theories and Models: An Overview of Their Current Status (pp. 247–278). Hillsdale, NJ: Erlbaum.
Cronbach, L. J. & Snow, R. E. (1977).Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Irvington.
De Jong, T. (1986).Kennis en Het Oplossen van Vakinhoudelijke Problemen [Knowledge Based Problem Solving], Dissertation. Eindhoven: Technical University Eindhoven.
De Jong, T. (1991). Learning and instruction with computer simulations.Education & Computing 6: 215–227.
DiSessa, A. A. (1987). Artificial worlds and real experience, in R. W. Lawler & M. Yazdani, eds.,Artificial Intelligence and Education. Volume One: Learning Environments and Tutoring Systems (pp. 55–77). Norwood: Ablex Publishing Corporation.
Elshout, J. J. (1976).Karakteristieke Moeilijkheden in Het Denken. Dissertation. Amsterdam: University of Amsterdam.
Elshout, J. J. (1987). Problem solving and education, in E. de Coite, H. Lodewijks, R. Parmentier & P. Span, eds.,Learning and Instruction (pp. 259–273). Oxford: Pergamon Books Ltd. Leuven: University Press.
Elshout, J. J. & Veenman, M. V. J. (1992). Relation between intellectual ability and working method as predictors of learning.Journal of Educational Research 85: 134–143.
Elshout, J. J. & Veenman, M. V. J. & Van Hell, J. G. (1993). Using the computer as a help tool during learning by doing.Computers & Education 21: 115–122.
Elshout-Mohr, M. (1992). Metacognitie van lerenden in onderwijsleerprocessen (Metacognition of learners in educational learning processes).Tijdschrifl voor Onderwijsresearch 17: 273–302.
Flavell, J. H. (1979). Metacognition and cognitive monitoring.American Psychologist 34: 906–911.
Goodyear, P., Njoo, M., Hijne, H. & Van Berkum, J. J. A. (1991). Learning processes, learner attributes and simulations.Education & Computing 6: 263–304.
Guilford, J. P. (1967).The Nature of Human Intelligence. New York: McGraw-Hill.
Inhelder, B. & Piaget, J. (1958).The Growth of Logical Thinking. London: Routledge & Kegan Paul.
Klahr, D. & Dunbar, K. (1988). Dual space search during scientific reasoning.Cognitive Science 12: 1–48.
Kunz, G. C., Drewniak, U. & Schott, F. (1992). On-line and off-line assessment of self-regulation in learning from instructional text.Learning and Instruction 2: 287–301.
Lohman, D. F. (1990). When good programs have bad effects on good students: Understanding mathemathanic effects in thinking skills programs, in M. J. Ippel & J. J. Elshout, eds.,Training van Hogere-Orde-Denkprocessen(pp. 21–30). Lisse: Swets & Zeitlinger.
Markman, E. M. (1985). Comprehension monitoring: Development and educational issues, in S. J. Chipman, J. W. Segal & R. Glaser, eds.,Thinking and Learning Skills: Research and Open Questions, Vol. 2 (pp. 275–291). Hillsdale NJ: Erlbaum.
Mettes, C. T. C. W. & Pilot, A. (1980).Over Het Leren Oplossen van Natuurwetenschappelijke Problemen. Dissertation. Enschede: Twente University.
Mettes, C. T. C. W., Pilot, A. & Roossink, H. J. (1981). Linking factual and procedural knowledge in solving science problems: a case study in a thermodynamics course.Instructional Science 10: 333–361.
Njoo, M. & De Jong, T. (1991). Learning processes of students working with a computer simulation in mechanical engineering, in M. Carretero, M. Pope, R. Simons & J. I. Pozo, eds.,Learning and Instruction: European Research in an International Context. Volume III (pp. 483–499). Oxford: Pergamon Press.
Njoo, M. & De Jong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support.Journal of Research in Science Teaching.
Reimann, P. (1989). Modeling scientific discovery learning processes with adaptive production systems, in D. J. Bierman, J. Breuker & J. Sandberg, eds.,Artificial Intelligence and Education: Synthesis and Reflection (pp. 218–227). Amsterdam: IOS.
Resnick, L. B. & Glaser, R. (1976). Problem solving and intelligence, in L. B. Resnick, ed.,The Nature of Intelligence (pp. 205–230). Hillsdale NJ: Erlbaum.
Shute, V. J. (1992). Aptitude-treatment interactions and cognitive skill diagnosis, in J. W. Regian & V. J. Shute, eds.,Cognitive Approaches to Automated Instruction (pp. 15–47). Hillsdale, NJ: Erlbaum.
Shute, V. J. (1993a). A comparison of learning environments: All that glitters, in S. P. Lajoie & S. J. Deny, eds.,Computers as Cognitive Tools (pp. 47–74). Hillsdale, NJ: Erlbaum.
Shute, V. J. (1993b). A macroadaptive approach to tutoring.Journal of Artificial Intelligence in Education 4: 61–93.
Shute, V. J., Glaser, R. & Raghavan, K. (1989). Inference and discovery in an exploratory laboratory, in P. L. Ackerman, R. J. Sternberg & R. Glaser, eds.,Learning and Individual Differences (pp. 279–326). New York: Freeman.
Snow, R. E. (1980). Aptitude processes, in R. E. Snow, P. Federico & W. E. Montague, eds.,Aptitude, Learning, and Instruction. Volume 1: Cognitive Process Analysis of Aptitude (pp. 27–63). Hillsdale, NJ: Erlbaum.
Snow, R. E. (1989). Aptitude-treatment interaction as a framework for research on individual differences in learning, in P. L. Ackerman, R. J. Sternberg & R. Glaser, eds.,Learning and Individual Differences (pp. 13–59). New York: Freeman.
Snow, R. E. & Lohman, D. F. (1984). Toward a theory of cognitive aptitude for learning from instruction.Journal of Educational Psychology 76: 347–376.
Snow, R. E. & Yalow, E. (1982). Education and intelligence, in R. J. Sternberg, ed.,Handbook of Human Intelligence (pp. 493–585). Cambridge: Cambridge University Press.
Sternberg, R. J. (1990).Metaphors of the Mind: Conceptions of the Nature of Intelligence. Cambridge: Cambridge University Press.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning.Cognitive Science 12: 257–285.
Van Berkum, J. J. A. & De Jong, T. (1991). Instructional environments for simulations.Education & Computing 6: 305–358.
Van Joolingen, W. R. (1993).Understanding and Facilitating Discovery Learning in Computer-Based Simulation Environments. Dissertation. Eindhoven: Technical University Eindhoven.
Veenman, M. V. J. (1993).Intellectual Ability and Metacognitive Skill: Determinants of Discovery Learning in Computerized Learning Environments. Dissertation. Amsterdam: University of Amsterdam.
Veenman, M. V. J. & Elshout, J. J. (1991). Intellectual ability and working method as predictors of novice learning.Learning and Instruction 1: 303–317.
Veenman, M. V. J. & Elshout, J. J. (1992). Intelligence en metacognitieve vaardigheden [Intelligence and metacognitive skills].Tijdschrifi voor Onderwijsresearch 17: 290–302.
Veenman, M. V. J., Elshout, J. J. & Bierman, D. J. (1992). Structuredness of learning environment versus individual differences as predictors of learning, in P. A. M. Kommers, D. H. Jonassen & T. Mayes, eds.,Cognitive Tools for Learning (NATO ASI Series F, Vol. 81, pp. 227–239). Heidelberg: Springer-Verlag.
Veenman, M. V. J., Elshout, J. J. & Busato, V. V. (1994). Metacognitive mediation in learning with computer-based simulations.Computers in Human Behavior 10: 93–106.
Veenman, M. V. J., Elshout, J. J. & Groen, M. G. M. (1993). Thinking aloud: Does it affect regulatory processes in learning.Tijdschrifi voor Onderwijsresearch 18: 322–330.
Veenman, M. V. J., Elshout, J. J. & Hoeks, J. C. J. (1993). Determinants of learning in simulation environments across domains, in D. Towne, T. de Jong & H. Spada, eds.,Simulation-based Experiential Learning (pp. 235–249). Berlin: Springer-Verlag.
White, B. Y. & Frederiksen, J. R. (1987). Qualitative models and intelligent learning environments, in R. W. Lawler & M. Yazdani, eds.,Artificial Intelligence and Education. Volume One: Learning Environments and Tutoring Systems (pp. 281–305). Norwood: Ablex Publishing Corporation.
White, B. Y. & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments.Artificial Intelligence 42: 99–157.
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Veenman, M.V.J., Elshout, J.J. Differential effects of instructional support on learning in simultation environments. Instr Sci 22, 363–383 (1994). https://doi.org/10.1007/BF00891961
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DOI: https://doi.org/10.1007/BF00891961