A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms

Multimodal Technologies and Interaction, Journal Year: 2025, Volume and Issue: 9(1), P. 3 - 3

Published: Jan. 7, 2025

Massive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These are not adaptive enough to manage dynamic nature of MOOC environments, resulting inaccurate predictions ineffective interventions. Accordingly, require more sophisticated artificial intelligence that can address these limitations. Moreover, incorporating feature selection methods explainable AI techniques enhance interpretability models, making them actionable for educators course designers. This paper provides a comprehensive review various methodologies, focusing on strategies research gaps. It highlights growing environment potential technology-driven gains outcome accuracy. also discusses use advanced based machine learning, deep meta-heuristics approaches improve rates, optimise outcomes, provide personalised educational experiences.

Language: Английский

Evaluating the quality of digital education resources based on learners’ online reviews through topic modeling and opinion mining

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Language: Английский