Predicting student performance in interactive online question pools using mouse interaction features
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
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3375462.3375521