PSFK: A Student Performance Prediction Scheme for First-Encounter Knowledge in ITS
Yonghao Song, Yan Jin, Xiaohui Zheng, Haiyan Han, Yunqin Zhong, Xiaofang Zhao · Lecture notes in computer science · 2015
As a user modeling method, Bayesian Knowledge Tracing (BKT) has been extensively used in the area of Intelligent Tutoring Systems (ITS). Thereafter the various schemes based on BKT are proposed to model student knowledge state and learning process. However, these schemes seldom consider the situation when a student first encounters a knowledge component (KC). That is, the existing models cannot be applied directly to predict student performance on a first-encounter KC. To solve this issue, combined user-based collaborative filtering and BKT model, a novel student performance prediction scheme PSFK is proposed in this paper. The PSFK scheme contains three major steps: first, building BKT models for each student and each KC he or she has encountered; then, finding the top-k similar students for a specified student S ; finally, predicting S ’s response on first-encounter KC. We evaluate our scheme on a real-world data set (which contains 4883 students and 177 KCs). The experiments show that the student performance prediction results of the proposed PSFK are acceptable (the RMSE can be decreased to 0.403).