Students' performance prediction using multi-channel decision fusion

Abstract

A teacher or an artificial instructor, embedded in an intelligent tutoring system, is interested in predicting the performance of his/her students to better adjust the educational materials and strategies throughout the learning process. In this chapter, a multi-channel decision fusion approach, based on using the performance in "assignment categories", such as homework assignments, is introduced to determine the overall performance of a student. In the proposed approach, the data gathered are used to determine four classes of "expert", "good", "average", and "weak" performance levels. This classification is conducted on both overall performance and the performance in assignment categories. Then, a mapping from the performances in "assignment categories" is learned, and is used to predict the overall performance. The main advantage of the proposed approach is in its capability to estimate students' performance after a few assignments. Consequently, it can help the instructors better manage their class and adjust educational materials to prevent underachievement. © 2014 Springer International Publishing Switzerland.