Comparative Analysis of Self-Labeled Algorithms for Predicting MOOC Dropout: A Case Study

Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learner dropout. Unlike traditional supervised models that rely solely on labeled data, self-labeled methods iteratively exploit both labeled and unlabeled instances, alleviating the scarcity of annotations in large-scale educational datasets. Using real-world MOOC data, ten self-labeled algorithms, including self-training, co-training, and tri-training variants, were evaluated across multiple labeled ratios. The experimental results show that ensemble-based methods, such as Co-training Random Forest, Co-Training by Committee, and Relevant Random subspace co-training, achieve predictive accuracy comparable to that fully supervised baselines even with as little as 4% labeled data. Beyond predictive performance, the findings highlight the scalability and cost-effectiveness of self-labeled SSL as a data-driven approach for enhancing learner retention in massive online learning.