Discovering Unusual Study Patterns Using Anomaly Detection and XAI
Files
Date
2024-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
1427
Ending Page
Alternative Title
Abstract
Learning Analytics (LA) has been leveraged as a tool to analyze and improve educational processes by informing its stakeholders. LA for student profiling focuses on discovering learning patterns and trends based on diverse features extracted from trace data. Prior studies have used classical clustering methods to group students and understand the study patterns of each cluster. However, variations within the clusters are still large making it difficult to draw concrete insights into the relation between study behaviors and learning outcomes. In this work, we leverage anomaly detection and eXplainable AI techniques to distinguish between normal and abnormal study patterns and to possibly discover unexpected patterns that are not apparent from clustering alone. We perform external validation to check the generalizability and compare the insights on study patterns from our method to be at par with insights gained from previous studies.
Description
Keywords
Learning Analytics, anomaly detection, isolation forest, learning analytics, shap, xai
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.