Do we need to go Deep? Knowledge Tracing with Big Data

Thumbnail Image

Files

2101.08349.pdf (356.78 KB)

Links to Files

https://arxiv.org/abs/2101.08349

Permanent Link

http://hdl.handle.net/11603/21029

Collections

UMBC Information Systems Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

2021-01-20

Type of Work

journal articles preprints
Text

Department

Program

Citation of Original Publication

Mandalapu, Varun; Gong, Jiaqi; Chen, Lujie; Do we need to go Deep? Knowledge Tracing with Big Data; Machine Learning (2021); https://arxiv.org/abs/2101.08349

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Attribution 4.0 International (CC BY 4.0)

Subjects

Abstract

Interactive Educational Systems (IES) enabled researchers to trace student knowledge in different skills and provide recommendations for a better learning path. To estimate the student knowledge and further predict their future performance, the interest in utilizing the student interaction data captured by IES to develop learner performance models is increasing rapidly. Moreover, with the advances in computing systems, the amount of data captured by these IES systems is also increasing that enables deep learning models to compete with traditional logistic models and Markov processes. However, it is still not empirically evident if these deep models outperform traditional models on the current scale of datasets with millions of student interactions. In this work, we adopt EdNet, the largest student interaction dataset publicly available in the education domain, to understand how accurately both deep and traditional models predict future student performances. Our work observes that logistic regression models with carefully engineered features outperformed deep models from extensive experimentation. We follow this analysis with interpretation studies based on Locally Interpretable Model-agnostic Explanation (LIME) to understand the impact of various features on best performing model pre-dictions.