Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling

Abstract

Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections.