Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning

Abstract

Interactive learning environments facilitate learning by providing hints to fill gaps in the understanding of a concept. Studies suggest that hints are not used optimally by learners. Either they are used unnecessarily or not used at all. It has been shown that learning outcomes can be improved by providing hints when needed. An effective hint-taking prediction model can be used by learning environments to make adaptive decisions on whether to withhold or to pro-actively provide hints. Past work on student behavior modeling has focused extensively on the task of modeling a learner's state of knowledge over time, referred to as knowledge tracing. Other aspects of student behavior such as tendency to use hints has garnered limited attention. Past knowledge tracing models either ignore the questions where hints were taken or label hints taken as an incorrect response. We propose a multi-task memory-augmented deep learning model to jointly predict propensity of taking a hint and the knowledge tracing task. The model incorporates the effect of past responses as well as hints taken on both the tasks. We apply the model on two datasets -- ASSISTments 2009-10 skill builder dataset and Junyi Academy Math Practicing Log. The results show that deep learning models efficiently leverage the sequential information present in student responses. The proposed model significantly out-performs the past work on hint prediction by at least 12% points. Moreover, we demonstrate that jointly modeling the two tasks improves performance on both of these.