Student Modeling in the ACT Programming Tutor: Adjusting a Procedural Learning Model With Declarative Knowledge
This paper describes a successful effort to increase the predictive validity of student modeling in the ACT Programming Tutor (APT). APT is an intelligent tutor constructed around a cognitive model of programming knowledge. As the student works, the tutor estimates the student’s growing knowledge of the component production rules in a process called knowledge tracing. Knowledge tracing employs a simple two-state learning model and Bayesian updates and has proven quite accurate in predicting student posttest performance, although with a small systematic tendency to overestimate test performance. This paper describes a simple three-state model in which the student may acquire non-ideal programming rules that do not transfer to the test environment. A series of short tests assess students’ declarative knowledge and these assessments are used to adjust knowledge tracing in the tutor. The resulting model eliminates over-prediction of posttest performance and more accurately predicts individual differences among students.
KeywordsProcedural Knowledge Declarative Knowledge Intelligent Tutor System Expected Proportion Mastery Learning
Unable to display preview. Download preview PDF.
- Anderson, J. R., (1993). Rules of the Mind. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
- Bloom, B. S., (1968). Learning for mastery. In Evaluation Comment, 1. Los Angeles: UCLA Center for the Study of Evaluation of Instructional Programs.Google Scholar
- Carroll, J. B. (1963). A model of school learning. Teachers College Record 64:723–733.Google Scholar
- Corbett, A. T., and Anderson, J. R., and O’Brien, A. T. (1995). Student modeling in the ACT Programming Tutor. In Nichols, P., Chipman, S., and Brennan, B., eds., Cognitively Diagnostic Assessment. Hillsdale, NJ: Erlbaum.Google Scholar
- Corbett, A. T., and Anderson, J. R. (1995a). Knowledge decomposition and subgoal reification in the ACT Programming Tutor. Artificial Intelligence and Education 1995: The Proceedings of AI-ED 95. Charlottesville, VA: AACE.Google Scholar
- Corbett, A. T., and Knapp, S., (1996). Plan scaffolding: Impact on the process and product of learning. In Frasson, C., Gauthier, G., and Lesgold, A., eds., Intelligent Tutoring Systems: Third International Conference, ITS ’96. New York: Springer.Google Scholar
- Goldstein, I. P., (1982). The genetic graph: A representation for the evolution of procedural knowledge. In Sleeman, D., and Brown, J.S., eds., Intelligent Tutoring Systems. New York: Academic.Google Scholar