A Study on Real-Time Prediction of Course Dropout Students Using LMS Logs

In recent years, there has been a rise in attempts to predict students' credit acquisition using machine learning. However, many of these attempts have been evaluated through cross-validation within the same academic year and course after the semester ends and results are available. However, th...

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Bibliographic Details
Published in2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) pp. 224 - 230
Main Authors Ohkawauchi, Takaaki, Tanaka, Eriko
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2024
Subjects
Accuracy
dropout
Informatics
Input variables
Learning management systems
log data
Machine learning
Measurement
prediction
Predictive models
Prevention and mitigation
Real-time systems
Time-frequency analysis
Online AccessGet full text
DOI10.1109/IIAI-AAI63651.2024.00051

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Summary:In recent years, there has been a rise in attempts to predict students' credit acquisition using machine learning. However, many of these attempts have been evaluated through cross-validation within the same academic year and course after the semester ends and results are available. However, there is a practical need for methods to predict real-time credit acquisition and AI models that can be universally applied to different students and subjects. In this study, we developed a system to identify at-risk students in real time by leveraging constantly updated learning management system (LMS) logs. As a result, we confirmed that we can make continuous and increasingly accurate predictions for different students as the new academic year progresses.
DOI:10.1109/IIAI-AAI63651.2024.00051