Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2020

Abstract

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.

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 10th International Learning Analytics & Knowledge Conference (LAK20), Frankfurt, Germany, 2020 March 23-27

First Page

645

Last Page

654

Identifier

10.1145/3375462.3375521

City or Country

Virtual Conference

Additional URL

https://doi.org/10.1145/3375462.3375521

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