ArticlePeerJ. Computer science2024
Predicting student success in MOOCs: a comprehensive analysis using machine learning models.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Where do we go from here? Evaluating the effectiveness of a nursing MOOC over six years.BMC medical education · 2026Article
- Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach.PloS one · 2026Article
- A temporal attention-based hybrid deep learning model for student performance and academic risk prediction.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study was motivated by the increasing popularity of Massive Open Online Courses (MOOCs) and the challenges they face, such as high dropout and failure rates. The existing knowledge primarily focused on predicting student dropout, but this study aimed to go beyond that by predicting both student dropout and course results. By using machine learning models and analyzing various data sources, the study sought to improve our understanding of factors influencing student success in MOOCs. Objectives: The primary aim of this research was to develop accurate predictions of students' course outcomes in MOOCs, specifically whether they would pass or fail. Unlike previous studies, this study took into account demographic, assessment, and student interaction data to provide comprehensive predictions. Methods: The study utilized demographic, assessment, and student interaction data to develop predictive models. Two machine learning methods, logistic regression, and random forest classification were employed to predict students' course outcomes. The accuracy of the models was evaluated based on four-class classification (predicting four possible outcomes) and two-class classification (predicting pass or fail). Results and Conclusions: The study found that simple indicators, such as a student's activity level on a given day, could be as effective as more complex data combinations or personal information in predicting student success. The logistic regression model achieved an accuracy of 72.1% for four-class classification and 92.4% for 2-class classification, while the random forest classifier achieved an accuracy of 74.6% for four-class classification and 95.7% for two-class classification. These findings highlight the potential of machine learning models in predicting and understanding students' course outcomes in MOOCs, offering valuable insights for improving student engagement and success in online learning environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.