Abstract
This paper describes a new student modeling paradigm called SMART. The premise is that a single, principled approach to student modeling, involving both theoretical and empirical methods, can render automated instruction more efficacious across a broad array of instructional domains. After defining key terms and discussing limitations to previous student modeling paradigms, I describe the SMART approach, as embedded within a statistics tutor called Stat Lady (Shute and Gluck, 1994). SMART works in conjunction with a tutor design where low-level knowledge and skills (i.e., curricular elements) are identified and separated into three main outcome types. Throughout the tutor, curricular elements with values below a pre-set mastery criterion are instructed, evaluated, and remediated, if necessary. The diagnostic part of the student model is driven by a series of regression equations based on the level of assistance the computer gives each person, per curriculum element. Remediation on a given element occurs when a subject fails to achieve mastery during assessment, which follows instruction. Remediation is precise because each element knows its location within the tutor where it is instructed and assessed. I end with a summary of results from two controlled evaluations of SMART examining the following research issues: (a) diagnostic validity, (b) individual differences in learning from Stat Lady, (c) affective perceptions of the tutorial experience, and (d) contributions of mastery and remediation to learning outcome and efficiency. Comments about related and future research with this paradigm are offered.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ackerman, P. L.: 1988, ‘Determinants of individual differences during skill acquisition: Cognitive abilities and information processing’.Journal of Experimental Psychology: General 117, 288–318.
Ackerman, P. L.: 1992, ‘Predicting individual differences in complex skill acquisition: Dynamics of ability determinants’.Journal of Applied Psychology 77, 598–614.
Alexander, P. A. and J. E. Judy: 1988, ‘The Interaction of Domain-Specific and Strategic Knowledge in Academic Performance’.Review of Educational Research 58(4), 375–404.
Anderson, J. R.: 1983,The Architecture of Cognition. Cambridge, MA: Harvard University Press.
Anderson, J. R.: 1987, ‘Skill Acquisition: Compilation of Weak-Method Problem Solutions’.Psychological Review 94, 192–210.
Anderson, J. R.: 1993,Rules of the Mind. Hillsdale, NJ: Erlbaum.
Bryan, W. L. and N. Harter: 1899, ‘Studies on the telegraphic language: The acquisition of a hierarchy of habits’.Psychological Review 6, 345–375.
Bunderson, C. V. and J. B. Olsen: 1983, ‘Mental Errors in Arithmetic Skills: Their Diagnosis in Precollege Students’. Final Project Report No. NSF SED 80-125000: WICAT Education Institution, Provo, UT.
Carlson, R. A., and R. G. Yaure: 1990, ‘Practice schedules and the use of component skills in problem solving’.Journal of Experimental Psychology: Learning, Memory, & Cognition 16, 484–496.
Clancey, W. J.: 1986, ‘Intelligent Tutoring Systems: A Tutorial Survey’. Report No. KSL-86-58: Stanford University, Stanford, CA.
Cronbach, L. J. and R. E. Snow: 1977,Aptitudes and Instructional Methods.:A Handbook for Research on Interactions. New York: Irvington.
Derry, S. J. and S. P. Lajoie: 1993, ‘A Middle Camp for (Un)Intelligent Instructional Computing: An Introduction’. In: S. P. Lajoie and S. J. Derry (eds.),Computers as Cognitive Tools. Hillsdale, NJ: Erlbaum, pp. 1–11.
Dillenbourg, P. and J. Self: 1992, ‘A Framework for Learner Modelling’.Interactive Learning Environments 2(2), 111–137.
Dochy, F. J. R. C.: 1992,Assessment of Prior Knowledge as a Determinant for Future Learning. Heerlen: Open University of the Netherlands.
Fisk, A. D. and W. A. Rogers: 1992, ‘The application of consistency principles for the assessment of skill development’. In: J. W. Regian and V. J. Shute (eds.),Cognitive approaches to automated instruction. Hillsdale, NJ: Erlbaum, pp. 171–194.
Glaser, R.: 1984, ‘Education and Thinking: The Role of Knowledge’.American Psychologist 39(2), 93–104.
Kulik, J. A., C. C. Kulik and R. L. Bangert-Drowns: 1985, ‘Effectiveness of computer-based education in elementary schools’.Computers in Human Behavior 1, 59–74.
Kyllonen, P. C.: 1994, ‘A Theoretical Framework for Cognitive Abilities Measurement’. In: D. Detterman (ed.),Current Topics in Human Intelligence: Volume IV, Theories of Intelligence. Norwood, NJ: Ablex.
Kyllonen, P. C. and R. E. Christal: 1989, ‘Cognitive Modeling of Learning Abilities: A Status Report of LAMP’. In: R. Dillon and J. W. Pellegrino (eds.),Testing: Theoretical and Applied Issues. San Francisco: Freeman, pp. 112–137.
Kyllonen, P. C. and V. J. Shute: 1989, ‘A Taxonomy of Learning Skills’. In: P. L. Ackerman, R. J. Sternberg and R. Glaser (eds.),Learning and Individual Differences. New York: Freeman, pp. 117–163.
Kyllonen, P. C. and W. C. Tirre: 1988, ‘Individual Differences in Associative Learning and Forgetting’.Intelligence 12, 393–421.
Kyllonen, P. C., D. J. Woltz, R. E. Christal, W. C. Tirre, V. J. Shute and S. Chaiken: 1990, ‘CAM-4: Computerized Battery of Cognitive Ability Tests’. Unpublished computer program, Brooks Air Force Base, Texas.
Martin, J. D., and K. VanLehn: 1993, ‘OLAE: Progress toward a multi-activity, Bayesian student modeler’. In: P. Brna, S. Ohlsson and H. Pain (eds.),Artificial Intelligence in Education, 1993. Charlottesville, VA: AACE, pp. 410–417.
Munro, A.: 1993, ‘Authoring Interactive Graphical Models for Instruction’. In: D. M. Towne, T. de Jong and H. Spada (eds.),Simulation-Based Experiential Learning. Berlin: Springer-Verlag, Series F, Vol. 122, pp. 33–46.
Noetic Systems: 1993, ‘ERGO v. 1.2’ (software), Baltimore, MD.
Regian, J. W. and V. J. Shute (eds.): 1992,Cognitive Approaches to Automated Instruction. Hillsdale, NJ: Erlbaum.
Schneider, W. and R. M. Shiffrin: 1977, ‘Controlled and automatic human information processing: Detection, search, and attention’.Psychological Review 84, 1–66.
Self, J. A.: 1989, ‘The case for formalising student models (and intelligent tutoring systems generally)’. In: D. Bierman, J. Breuker and J. Sandberg (eds.),Artificial intelligence and education: Synthesis and reflection. Springfield, VA: IOS, p. 244.
Shute, V. J.: 1991, ‘Who is Likely to Acquire Programming Skills?’.Journal of Educational Computing Research 7, 1–24.
Shute, V. J.: 1992, ‘Aptitude-Treatment Interactions and Cognitive Skill Diagnosis’. In: J. W. Regian and V. J. Shute (eds.),Cognitive Approaches to Automated Instruction. Hillsdale, NJ: Erlbaum, pp. 15–47.
Shute, V. J.: 1993a, ‘A Comparison of Learning Environments: All that Glitters...’. In: S. P. Lajoie and S. J. Derry (eds.),Computers as Cognitive Tools. Hillsdale, NJ: Erlbaum, pp. 47–74.
Shute, V. J.: 1993b, ‘A Macroadaptive Approach to Tutoring’.Journal of Artificial Intelligence and Education 4(1), 61–93.
Shute, V. J.: 1994, ‘Learning Processes and Learning Outcomes’. In: T. Husen and T. N. Postlethwaite (eds.),International Encyclopedia of Education (2nd Edition). New York, NY: Pergamon Press, pp. 3315–3325.
Shute, V. J. and L. A. Gawlick-Grendell: 1994, ‘What does the computer contribute to learning?’.Computers and Education: An International Journal 23(3), 177–186.
Shute, V. J. and K. A. Gluck: 1994, ‘Stat Lady: Descriptive Statistics Module’. Unpublished computer program, Armstrong Laboratory, Brooks Air Force Base, Texas.
Shute, V. J. and P. C. Kyllonen: 1990, ‘Modeling Individual Differences in Programming Skill Acquisition’. Technical Paper AFHRL-TP-90-76. Armstrong Laboratory, Brooks Air Force Base, Texas.
Shute, V. J. and J. Psotka: in press, ‘Intelligent Tutoring Systems: Past, Present, and Future’. To appear in D. Jonassen (ed.),Handbook of Research on Educational Communications and Technology. Scholastic Publications.
Shute, V. J. and J. W. Regian: 1993, ‘Principles for Evaluating Intelligent Tutoring systems’.Journal of Artificial Intelligence in Education 4(3), 245–271.
Shute, V. J., L. A. Gawlick-Grendell, R. K. Young and C. A. Burnham: in press, ‘An Experiential System for Learning Probability: Stat Lady Description and Evaluation’. To appear inInstructional Science.
Sleeman, D. H.: 1987, ‘PIXIE: A Shell For Developing Intelligent Tutoring Systems’. In: R. Lawler and M. Yazdani (eds.),AI and Education: Learning Environments and Intelligent Tutoring Systems (Vol. 1). Norwood, NJ: Ablex Publishing, pp. 239–265.
Sleeman, D. H. and J. S. Brown: 1982,Intelligent Tutoring Systems. London: Academic Press.
Sleeman, D. H., A. E. Kelly, R. Martinak, R. D. Ward and J. L. Moore: 1989, ‘Studies of Diagnosis and Remediation With High School Algebra Students’.Cognitive Science 13(4), 551–568.
Snow, R. E.: 1990, ‘Toward Assessment of Cognitive and Conative Structures in Learning’.Educational Researcher 18(9), 8–14.
Swanson, J. H.: 1990, ‘The Effectiveness of Tutorial Strategies: An Experimental Evaluation’. Paper presented at the annual conference of the American Educational Research Association, Boston, MA.
Swan, M. B.: 1983, ‘Teaching Decimal Place Value: A Comparative Study of Conflict and Positively-Only Approache‘ (Research Rep. No. 31). Nottingham, England: University of Nottingham, Sheel Center for Mathematical Education.
Towne, D. M.: 1993, ‘Teaching and Learning Diagnostic Skills in a Simulation Environment’. In: D. M. Towne, T. de Jong and H. Spada (eds.),Simulation-Based Experiential Learning. Berlin: Springer-Verlag, Series F, Vol. 122, pp. 149–164.
VanLehn, K.: 1990,Mind Bugs: The Origins of Procedural Misconceptions. Cambridge, MA: MIT Press.
Wenger, E.: 1987,Artificial Intelligence and Tutoring Systems. Los Altos: CA, Morgan Kaufmann Publishers.
White, B. Y. and J. R. Frederiksen: 1987, ‘Qualitative Models and Intelligent Learning Environments‘. In: R. Lawler and M. Yazdani (eds.),AI and Education. Norwood, NJ: Ablex Publishing, pp. 281–305.
Woltz, D. J.: 1988, ‘An investigation of the role of working memory in procedural skill acquisition’.Journal of Experimental Psychology: General 117, 319–331.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Shute, V.J. SMART: Student modeling approach for responsive tutoring. User Model User-Adap Inter 5, 1–44 (1995). https://doi.org/10.1007/BF01101800
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF01101800