Abstract
Learning is widely viewed as a knowledge communication process coupled with a knowledge compilation process (Anderson, 1985). The communication process interprets instruction, thereby incorporating new information from the environment into the mental structures of the student. Knowledge compilation occurs with practice. It transforms the initial mental structures into a form that makes performance faster and more accurate. Moreover, the transformed mental structures are less likely to be forgotten. At one time, psychology concerned itself exclusively with the compilation process by using such simple stimuli (e.g., nonsense syllables) that the effects of the communication process could be ignored. The work presented here uses more complicated stimuli, the calculational procedures of ordinary arithmetic. For such stimuli, the effects of the knowledge communication process cannot be ignored. Later in this chapter it is shown that certain types of miscommunication can cause students to have erroneous conceptions. The long-term objective of the research reported here is to develop a theory of the neglected half of learning, knowledge communication. The experimental methods employed are designed to show the effects of knowledge communication and hide the effects of knowledge compilation. Consequently, whenever the term learning appears, it is intended to mean knowledge communication.
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References
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard.
Anderson, J. R. (1985). Cognitive psychology and its implications. New York: Freedman.
Anderson, J. R., Farrell, R., & Saurers, R. (1984). Learning to program in LISP. Cognitive Science, 8, 87–129.
Anzai, Y., & Simon, H. A. (1979). The theory of learning by doing. Psychological Review, 86, 124–140.
Ashlock, R. B. (1976). Error patterns in computation. Columbus, OH: Bell & Howell.
Berwick, R. (1985). The acquisition of syntactic knowledge. Cambridge, MA: MIT Press.
Brown, J. S., & Burton, R. B. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.
Brown, J. S., & VanLehn, K, (1980). Repair Theory: A generative theory of bugs in procedural skills. Cognitive Science, 4, 379–426.
Brownell, W. A. (1935). Psychological considerations in the learning and teaching of arithmetic. In W. D. Reeve (Ed.), The teaching of arithmetic. New York: Teachers College, Bureau of Publications.
Brueckner, L. J. (1930). Diagnostic and remedial teaching in arithmetic. Philadelphia: Winston.
Burton, R. B. (1982). Diagnosing bugs in a simple procedural skill. In D. H. Sleeman, and J. S. Brown (Eds.) Intelligent Tutoring Systems. New York: Academic Press.
Buswell, G. T. (1926). Diagnostic studies in arithmetic. Chicago: University of Chicago Press.
Cox, L. S. (1975). Diagnosing and remediating systematic errors in addition and subtraction computation. The Arithmetic Teacher, 22, 151–157.
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986). Chunking in SOAR: The anatomy of a general learning mechanism. Machine Learning, 1, 11–46.
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33, 1–64.
Lankford, F. G. (1972). Some computational strategies of seventh grade pupils. Charlottesville, VA: University of Virginia.
Minton, S. (1985). Selectively generalizing plans for problem-solving. In Proceedings of IJCAI 85 (pp. 596–599). Los Altos, CA: Morgan-Kaufman.
Newell, A. (1980). Reasoning, problem solving and decision processes: The problem space as a fundamental category. In R. Nickerson (Ed.), Attention and Performance VIII. Hillsdale, NJ: Erlbaum.
Newell, A., & Simon, H. A. (1972). Human problem-solving. Englewood Cliffs, NJ: Prentice-Hall.
Norman, D. A. (1981). Categorization of action slips. Psychological Review, 88, 1–15.
Resnick, L. (1982). Syntax and semantics in learning to subtract. In T. Carpenter, J. Moser & T. Romberg (Ed.). Addition and subtraction: A cognitive perspective. Hillsdale, NJ: Lawrence Erlbaum Assoc.
Resnick, L. B., & Omanson, S. F. (1987). Learning to understand arithmetic. In R. Glaser (Ed.), Advances in instructional psychology. Hillsdale, NJ: Lawrence Erlbaum Assoc.
Roberts, G. H. (1968). The failure strategies of third grade arithmetic pupils. The Arithmetic Teacher, 15, 442–446.
Schank, R. (1982). Dynamic memory: A theory of learning in computers and people. Cambridge, England: Cambridge University Press.
Shaw, D. J., Standiford, S. N., Klein, M. F., & Tatsuoka, K. K. (1982). Error analysis of fraction arithmetic-selected case studies (Tech. Report 82–2-NIE). Urbana, IL: University of Illinois, Computer-based Education Research Laboratory.
Siegler, R. S., & Shrager, J. (1984). Strategy choices in addition: How do children know what to do? In C. Sophian (Ed.). Origins of Cognitive Skill. Hillsdale, NJ: Lawrence Erlbaum Assoc.
Sleeman, D. (1984). An attempt to understand students’ understanding of basic algebra. Cognitive Science, 8, 387–412.
Sleeman, D. H. (1985). Basic algebra revisited: A study with 14-year olds. International Journal of Man-Machine Studies, 22, 127–149.
Sleeman, D. H., & Smith, M. J. (1981). Modeling student’s problem solving. Artificial Intelligence, 16, 171–187.
Smith, B. C. (1982). Reflection and semantics in a procedural language (Technical Report MIT-TR-272). Cambridge, MA: M.I.T. Laboratory for Computer Science.
Tatsuoka, K. K., & Baillie, R. (1982). Rule space, the product space of two score components in signed-number subtraction: An approach to dealing with inconsistent use of erroneous rules (Tech. Report 82–3-ONR). Urbana, IL: University of Illinois, Computer-based Education Research Laboratory.
VanLehn, K. (1982). Bugs are not enough: Empirical studies of bugs, impasses and repairs in procedural skills. The Journal of Mathematical Behavior, 3, 3–71.
VanLehn, K. (1983a). Felicity conditions for human skill acquisition: Validating an AI-based theory (Tech. Report CIS-21). Palo Alto, CA: Xerox Palo Alto Research Center.
VanLehn, K. (1983b). Human skill acquisition: Theory, model and psychological validation. In Proceedings of AAAI-83 (pp. 420–423). Los Altos, CA: Kaufman.
VanLehn, K. (1986). Arithmetic procedures are induced from examples. In J. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics. Hillsdale. NJ: Lawrence Erlbaum Assoc.
VanLehn, K. (in press). Cognitive procedures: The acquisition and mental representation of basic mathematical skills. Cambridge, MA: MIT Press.
VanLehn, K., Brown, J. S., & Greeno, J. G. (1984). Competitive argumentation in computational theories of cognition. In W. Kintsch, J. Miller, & P. Poison (Ed.), Methods and tactics in cognitive science. Hillsdale, NJ: Lawrence Erlbaum, Assoc.
Wexler, K., & Culicover, P. (1980). Formal principles of language acquisition. Cambridge, MA: MIT Press.
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VanLehn, K. (1988). Toward a Theory of Impasse-Driven Learning. In: Mandl, H., Lesgold, A. (eds) Learning Issues for Intelligent Tutoring Systems. Cognitive Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-6350-7_2
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DOI: https://doi.org/10.1007/978-1-4684-6350-7_2
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