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Volumn 38, Issue 1, 2000, Pages 157-180

Multistrategy discovery and detection of novice programmer errors

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; ERROR DETECTION; KNOWLEDGE REPRESENTATION; PROBLEM SOLVING; PROGRAM DIAGNOSTICS; PROLOG (PROGRAMMING LANGUAGE);

EID: 0033884171     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1007690108308     Document Type: Article
Times cited : (33)

References (29)
  • 1
    • 0001882773 scopus 로고    scopus 로고
    • Refinement-based student modeling and automated bug library construction
    • Baffes, P. & Mooney, R. (1996). Refinement-based student modeling and automated bug library construction. Journal of Artificial Intelligence in Education, 7, 75-116.
    • (1996) Journal of Artificial Intelligence in Education , vol.7 , pp. 75-116
    • Baffes, P.1    Mooney, R.2
  • 4
    • 0343442766 scopus 로고
    • Knowledge acquisition via incremental conceptual clustering
    • Fisher, T. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139-172.
    • (1987) Machine Learning , vol.2 , pp. 139-172
    • Fisher, T.1
  • 8
    • 0025385101 scopus 로고
    • Understanding and debugging novice programs
    • Johnson, W. L. (1990). Understanding and debugging novice programs. Artificial Intelligence, 42, 51-97.
    • (1990) Artificial Intelligence , vol.42 , pp. 51-97
    • Johnson, W.L.1
  • 10
    • 46149142725 scopus 로고
    • Integrated learning: Controlling explanation
    • Lebowitz, M. (1986). Integrated learning: Controlling explanation. Cognitive Science, 10, 219-240.
    • (1986) Cognitive Science , vol.10 , pp. 219-240
    • Lebowitz, M.1
  • 11
    • 0000166613 scopus 로고
    • Experiments with incremental concept formation: UNIMEM
    • Lebowitz, M. (1987). Experiments with incremental concept formation: UNIMEM. Machine Learning, 2, 103-138.
    • (1987) Machine Learning , vol.2 , pp. 103-138
    • Lebowitz, M.1
  • 12
    • 0038823372 scopus 로고
    • Automatic debugging of Prolog programs in a Prolog intelligent tutoring system
    • Looi, C. (1991). Automatic debugging of Prolog programs in a Prolog intelligent tutoring system. Instructional Science, 20, 215-263.
    • (1991) Instructional Science , vol.20 , pp. 215-263
    • Looi, C.1
  • 13
    • 0142032695 scopus 로고
    • The central importance of student modeling to intelligent tutoring
    • E. Costa (Ed.), Berlin, Springer Verlag
    • McCalla, G. (1992). The central importance of student modeling to intelligent tutoring. In E. Costa (Ed.), New directions for intelligent tutoring systems, Berlin, Springer Verlag.
    • (1992) New Directions for Intelligent Tutoring Systems
    • McCalla, G.1
  • 14
    • 0003046842 scopus 로고
    • Learning from observation: Conceptual clustering
    • R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Palo Alto, CA: Tioga
    • Michalski, R. & Stepp, R. (1983). Learning from observation: conceptual clustering. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: an artificial intelligence approach. Palo Alto, CA: Tioga.
    • (1983) Machine Learning: An Artificial Intelligence Approach
    • Michalski, R.1    Stepp, R.2
  • 15
  • 16
    • 0343541518 scopus 로고    scopus 로고
    • The role of explanation in elementary physics learning
    • This issue
    • Neri, F. (1999). The role of explanation in elementary physics learning. Machine Learning, This issue.
    • (1999) Machine Learning
    • Neri, F.1
  • 17
    • 0006460167 scopus 로고
    • Learning causal patterns: Making a transition from data-driven to theory-driven learning
    • R. Michalski & G. Tecuci (Eds.), San Francisco, CA: Morgan Kaufmann
    • Pazzani, M. (1994). Learning causal patterns: making a transition from data-driven to theory-driven learning. In R. Michalski & G. Tecuci (Eds.), Machine learning: a multistrategy approach (Vol. IV, pp. 267-293). San Francisco, CA: Morgan Kaufmann.
    • (1994) Machine Learning: A Multistrategy Approach , vol.4 , pp. 267-293
    • Pazzani, M.1
  • 18
    • 0001602577 scopus 로고
    • A note on inductive generalization
    • Plotkin, G. (1970). A note on inductive generalization. Machine Intelligence, 5, 153-163.
    • (1970) Machine Intelligence , vol.5 , pp. 153-163
    • Plotkin, G.1
  • 19
    • 0029308579 scopus 로고
    • Automated refinement of first-order Horn-clause domain theories
    • Richards, B. & Mooney, R. (1995). Automated refinement of first-order Horn-clause domain theories. Machine Learning, 19, 95-131.
    • (1995) Machine Learning , vol.19 , pp. 95-131
    • Richards, B.1    Mooney, R.2
  • 24
    • 0025388980 scopus 로고
    • Extending domain theories: Two case studies in student modeling
    • Sleeman, D., Hirsh, H., Ellery, I., & Kim, I. (1990). Extending domain theories: Two case studies in student modeling. Machine Learning, 5, 11-37.
    • (1990) Machine Learning , vol.5 , pp. 11-37
    • Sleeman, D.1    Hirsh, H.2    Ellery, I.3    Kim, I.4
  • 27
    • 58149411184 scopus 로고
    • Features of similarity
    • Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327-352.
    • (1977) Psychological Review , vol.84 , pp. 327-352
    • Tversky, A.1
  • 28
    • 0023151233 scopus 로고
    • Learning one procedure per lesson
    • VanLehn, K. (1987). Learning one procedure per lesson. Artificial Intelligence, 31, 1-40.
    • (1987) Artificial Intelligence , vol.31 , pp. 1-40
    • VanLehn, K.1
  • 29
    • 0001237155 scopus 로고
    • On the interaction of theory and data in concept learning
    • Wisniewski, E. & Medin, D. (1994). On the interaction of theory and data in concept learning. Cognitive Science, 18, 221-281.
    • (1994) Cognitive Science , vol.18 , pp. 221-281
    • Wisniewski, E.1    Medin, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.