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

Abstract

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.