  Modeling student learning and further predicting the performance is a
well-established task in online learning and is crucial to personalized
education by recommending different learning resources to different students
based on their needs. Interactive online question pools (e.g., educational game
platforms), an important component of online education, have become
increasingly popular in recent years. However, most existing work on student
performance prediction targets at online learning platforms with a
well-structured curriculum, predefined question order and accurate knowledge
tags provided by domain experts. It remains unclear how to conduct student
performance prediction in interactive online question pools without such
well-organized question orders or knowledge tags by experts. In this paper, we
propose a novel approach to boost student performance prediction in interactive
online question pools by further considering student interaction features and
the similarity between questions. Specifically, we introduce new features
(e.g., think time, first attempt, and first drag-and-drop) based on student
mouse movement trajectories to delineate students' problem-solving details. In
addition, heterogeneous information network is applied to integrating students'
historical problem-solving information on similar questions, enhancing student
performance predictions on a new question. We evaluate the proposed approach on
the dataset from a real-world interactive question pool using four typical
machine learning models.
