Predicting student dropouts with machine learning: An empirical study in Finnish higher education

Matti Vaarma and Hongxiu Li

Technology in Society, 2024, vol. 76, issue C

Abstract: This study uses three machine learning models to predict student dropouts based on students' transcript, demographic, and learning management system (LMS) data from a Finnish university. The contribution of this research lies in 1) comparing the relative importance of LMS (Moodle) data with transcript and demographic data in degree program dropout prediction, 2) examining the predictive importance of different data features monthly as a function of time from enrollment, hence extending the prior end-of-semester research to a midsemester analysis, and 3) measuring the prediction performance of the models monthly. The results identify “accumulated credits” (transcript) the “number of failed courses” (transcript), and “Moodle activity count” (LMS) as the most important features, suggesting LMS has significant predictive power and should be considered alongside transcript and demographic data when predicting degree program dropouts. Moreover, we visualize how these factors' importance and prediction performance vary over time, revealing general longitudinal trends and fluctuations within semesters. Finally, we elaborate upon this study's contributions before highlighting its limitations.

Keywords: Student dropout prediction; Learning management systems; Machine learning; Higher education; Data analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160791X24000228
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:76:y:2024:i:c:s0160791x24000228

DOI: 10.1016/j.techsoc.2024.102474

Access Statistics for this article

Technology in Society is currently edited by Charla Griffy-Brown

More articles in Technology in Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().