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The computer as a tutor — can it adapt to the individual learner?

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Abstract

Educational literature indicates that computers have not yet completely fulfilled the promise for systematic individualization of instruction. We offer here a partial explanation for this, based on observations of elementary-school children doing computer-managed drill and practice in arithmetic in four different CAI systems. A general model for adaptation of instruction to the learner, using computerized management of practice, is presented. Our data show that the key elements of this process — the software-based students' evaluations and the subsequent decisions — are sometimes inaccurate or even totally wrong and result in presentation of inappropriate material or feedback to the learner. We suggest the incorporation of human teachers into the evaluation and decision-making process.

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References

  • Becker, H. J. (1984). Computers in schools today: some basic considerations. American Journal of Education, 93(1), 22–39.

    Google Scholar 

  • Becker, H. J. (1987). Using computers for instruction, Byte, 149–291.

  • Bork, A. (1987). The potential for interactive technology, Byte, 201–210.

  • Brown, J. S. and Burton, R. B. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.

    Google Scholar 

  • Burton, R. B. (1981). DEBUGGY: diagnosis of errors in basic mathematical skills. In D. H., Sleeman and J. S., Brown (Eds.), Intelligent tutoring systems. London: Academic Press.

    Google Scholar 

  • Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.

    Google Scholar 

  • Clark, R. E. (1985). Evidence for confounding in computer-based instruction studies: analyzing the meta-analyses. ECTJ, 33(4), 249–262.

    Google Scholar 

  • Cohen, V. B. (1985). A re-examination of feedback in computer-based instruction: implications for instructional design. Educational Technology, 25(1), 33–37.

    Google Scholar 

  • Corno, L. and Snow, R. E. (1986). Adapting teaching to individual differences among learners. In M. C., Wittrock (Ed.), Handbook of research on teaching (third edition). New York: Macmillan Publishing Company.

    Google Scholar 

  • Erlwanger, S. H. (1973). Benny's conception of rules and answers in IPI mathematics. The Journal of Children's Mathematical Behavior, 1(2), 2–26.

    Google Scholar 

  • Glaser, R. (1972). Individuals and learning: the new aptitudes. Educational Researcher, 1(6), 5–13.

    Google Scholar 

  • Glaser, R. (1977). Adaptive education: individual diversity and learning. New York: Holt, Rinehart and Winston.

    Google Scholar 

  • Hativa, N. (1986). Computer-based practice in arithmetic (TOAM): dreams and realities — an ethnographic study. The Pinchas Sapir Center for Development, Discussion paper No. 7-86.

  • Hativa, N. (1988a). Computer-based drill and practice in arithmetic — widening the gap between high and low achieving students. American Educational Research Journal, 25(3), 366–397.

    Google Scholar 

  • Hativa, N. (1988b). Sigal's software- and hardware-related errors in computer-assisted practice of arithmetic — a case study. Journal for Research in Mathematics Education, 19(4), 195–214.

    Google Scholar 

  • Hativa, N. (1988c). CAI vs paper and pencil — discrepancies in students' performance. Instructional Science, 17(1), 77–96.

    Google Scholar 

  • Hativa, N. (1989). Students' conceptions of and attitudes towards specific features of a CAI system. Journal of Computer-Based Instruction, 16(3), 81–89.

    Google Scholar 

  • Hativa, N., Swisa, S. and Lesgold, A. (1989). Competition in individualized CAI. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.

  • Kearsley, G. P. (1977). Some conceptual issues in computer-assisted instruction. Journal of Computer-Based Instruction, 4, 8–16.

    Google Scholar 

  • Klein, M. F., Birenbaum, M., Standiford, S. N. and Tatsuoka, K. K. (1981). Logical error analysis and construction of tests to diagnose student “bugs” in addition and subtraction of fractions. Report 81–6, the Computer-based Education Research Laboratory, University of Illinois, Urbana, Illinois.

    Google Scholar 

  • Kulhavy, R. W. (1977). Feedback in written instruction. Review of Educational Research, 47, 211–232.

    Google Scholar 

  • Kulik, J. A., Bangert, R. L. and Williams, G. W. (1983). Effects of computer-based teaching on secondary school students. Journal of Educational Psychology, 75(1), 19–26.

    Google Scholar 

  • Kulik, J. A. (1984). The fourth revolution in teaching: meta-analysis. Paper presented at the annual meeting of the American Educational Research Association, New Orleans.

  • Kulik, J. A. (1985). Consistencies in findings on computer-based education. Paper presented at the annual meeting of the American Educational Research Association, Chicago.

  • Lepper, M. R. and Chabay, R. W. (1985). Intrinsic motivation and instruction: conflicting views on the role of motivational processes in computer-based education. Educational Psychologist, 20(4), 217–230.

    Google Scholar 

  • Malone, T. W. and Lepper, M. R. (1987). Making learning fun: a taxonomy of intrinsic motivations for learning. In R. E., Snow and M. J., Farr (Eds.), Aptitude, learning and instruction (Volume 3: conative and affective process analyses). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • McLeod, D. B. (1988). Affective issues in mathematical problem solving: some theoretical considerations. Journal for Research in Mathematics Education, 19(2), 134–141.

    Google Scholar 

  • Mead, G. H. (1934). Mind, self and society. Chicago: University of Chicago Press.

    Google Scholar 

  • Miller, G. A., Galanter, E. and Pribram, K. (1960). Plans and the structure of behavior. New York: Holt, Rinehart and Winston.

    Google Scholar 

  • Niemiec, R. P. and Walberg, H. J. (1986). Computers and achievement in the elementary schools. Journal of Educational Computing Research, 1(4), 435–440.

    Google Scholar 

  • Osin, L. and Nesher, P. (1988). Comparison of student performance in arithmetic exercises: TOAM vs paper-and-pencil testing. International Journal of Man-Machine Studies, 31, 293–313.

    Google Scholar 

  • Perry, N. (1990). The relationship between students' advancement in practice problems in arithmetic, by computerized management of instruction and students' command of arithmetic material. Master thesis, Tel Aviv University (Hebrew).

  • Piaget, J. (1962). Play, dreams and imitation in childhood. New York: W. W. Norton.

    Google Scholar 

  • Schoenfeld, A. H. (1983). Beyond the purely cognitive: belief systems, social cognitions and meta cognitions as driving forces in intellectual performance. Cognitive Science, 7, 329–363.

    Google Scholar 

  • Schoenfeld, A. H. (1985). Mathematical problem solving. Orlando, FL: Academic Press.

    Google Scholar 

  • Schofield, J. W. (1985). The impact of an intelligent computer-based tutor on classroom social processes: an ethnographic study. Learning Research and Development Center, The University of Pittsburgh.

  • Silver, E. A. (1985). Research on teaching mathematical problem solving: some under-represented themes and needed directions. In E. A., Silver (Ed.), Teaching and learning mathematical problem solving: multiple research perspectives. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Simon, H. A. (1981). The sciences of the artificial. Cambridge, MA: MIT Press.

    Google Scholar 

  • Standiford, S. N., Klein, M. F. and Tatsuoka, K. K. (1982). Decimal fraction arithmetic: logical error analysis and its validation. Report 82–1, the Computer-based Education Research Laboratory, University of Illinois, Urbana, Illinois.

    Google Scholar 

  • Suppes, P. (1966). The uses of computers in education. Scientific American, 215, 206–220.

    Google Scholar 

  • Tarktinski, N. (1988). Effect of time pressure on speed and accuracy in performance on computerized general-knowledge test. Report No. 68. National Institute for Testing and Evaluation, Israel. (Hebrew).

    Google Scholar 

  • VanLehn, K. (1982). Bugs are not enough: empirical studies of bugs, impasses and repairs in procedural skills. Journal of Mathematical Behavior, 3(2), 3–71.

    Google Scholar 

  • Winn, W. (1987). Instructional design and intelligent systems: shifts in the designer's decision-making role. Instructional Science, 16(1), 59–77.

    Google Scholar 

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This paper was presented at the annual meeting of the American Educational Research Association, Boston, April, 1990.

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Hativa, N., Lesgold, A. The computer as a tutor — can it adapt to the individual learner?. Instr Sci 20, 49–78 (1991). https://doi.org/10.1007/BF00119686

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