When and why does mastery learning work: Instructional experiments with ACT-R “simstudents”

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2363, Issue , 2002, Pages 355-366

  Maclaren, Benjamin ^a   Koedinger, Kenneth ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED INSTRUCTION; CURRICULA; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; STUDENTS; TEACHING;

ANDERSONS; COGNITIVE MODEL; EXPERIMENT LEARNING; FORMATIVE EVALUATION; INSTRUCTIONAL DESIGNS; MASTERY LEARNING; POTENTIAL BENEFITS;

LEARNING SYSTEMS;

EID: 27844458523     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47987-2_39     Document Type: Conference Paper

References (11)

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2. Bloom, B. (1987). A Response to Slavin’s Mastery Learning Reconsidered, 57, 507-508.
3. Corbett, A.T. and Anderson, J. R. (1995). Knowledge decomposition and subgoal reification in the ACT programming tutor. Artificial Intelligence and Education, 1995: The Proceedings of AI-ED 95. Charlottesville, VA: AACE.
4. Koedinger, K.R., & MacLaren, B. A. (2002). Developing a Pedagogical Domain Theory of Early Algebra Problem Solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html
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7. Lovett, M. (1998). Choice. In The Atomic Components of Thought.
8. Anderson J.R. & Lebiere, C. (pp255-296) Laurence Erlbaum Associates.
9. Nathan, M. J. , Long, S. D. & Alibali, M. W. (in press). An analysis of the presentation of arithmetic and algebraic topics in common mathematics textbooks. In Preparation.
10. Slavin, R. (1987). Mastery learning reconsidered. Review of educational research, 57, 175-213.
11. VanLehn, K., Ohlsson, S., & Nason, R. (1994). Applications of simulated students: An exploration. Journal of Artificial Intelligence and Education, 5(2), 135-175.