User Modeling pp 243-254 | Cite as

Student Modeling in the ACT Programming Tutor: Adjusting a Procedural Learning Model With Declarative Knowledge

  • Albert T. Corbett
  • Akshat Bhatnagar
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)


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.


Procedural Knowledge Declarative Knowledge Intelligent Tutor System Expected Proportion Mastery Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • Albert T. Corbett
    • 1
  • Akshat Bhatnagar
    • 1
  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

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