The Prediction of Student First Response Using Prerequisite Skills

Abstract

A large amount of research in the field of educational data analytics has focused primarily on student next problem correctness. Although the prediction of such information is useful in assessing current student performance, it is better for teachers and instructors to place attention on student knowledge over a longer period of time. Several researchers have articulated that it is important to predict aspects that are more meaningful, inspiring our work here to utilize the large amounts of student data available to derive more substantial predictions over student knowledge. Our goal in this paper is to utilize prerequisite information to better predict student knowledge quantitatively as a subsequent skill is begun. Learning systems like ASSISTments and Khan Academy already record such prerequisite information, and can therefore be used to construct a method of prediction as described in this paper. Using these inter-skill relationships, our method estimates students' initial knowledge based on performance on each prerequisite skill. We compare our method with the standard Knowledge Tracing (KT) model and majority class in terms of the predictive accuracy of students' first responses on subsequent skills. Our results support our method as a viable means of representing student prerequisite knowledge in a subsequent skill, leading to results that outperform the majority class and that are comparably superior to KT by providing more definitive student knowledge estimates without sacrificing predictive accuracy.