Faculty Advisor

Joseph Beck

Faculty Advisor

Neil T. Heffernan


Intelligent Tutoring Systems (ITS) have been proven to be efficient in providing students assistance and assessing their performance when they do their homework. Many research projects have been done to analyze how students' knowledge grows and to predict their performance from within intelligent tutoring system. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore how they ask for hints and make attempts. In this research work, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for previous question to predict students' performance. A two step modeling methodology is put forward in the work, which is a combination of Tabling method and the Logistic Regression. We used an ASSISTments dataset of 66 students answering a total of 34,973 problems generated from 5010 questions over the course of two years. The experimental results showed that the Sequence of Action model has reliable predictive accuracy than Knowledge Tracing and Assistance Model and its performance of prediction is improved after combining with Knowledge Tracing.


Worcester Polytechnic Institute

Degree Name



Computer Science

Project Type


Date Accepted





Intelligent Tutoring System, Student Modelling, Educational Data Mining