Predictive Insights into Course Completion Rates in MOOCs

Authors: Syed Arham Akheel

DOI: https://doi.org/10.5281/zenodo.14280082

Short DOI: https://doi.org/g8ttfx

Country: USA

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Abstract: Massive Open Online Courses (MOOCs) have revolutionized higher education by providing scalable and flexible learning opportunities. However, completion rates remain consistently low, prompting researchers to investigate key predictors of student success. This study combines a literature review with an empirical experiment using machine learning models to predict course completion based on behavioural and demographic data. Decision Tree, Random Forest, and regression models are employed to identify features that significantly impact course outcomes. The results highlight the crucial role of engagement, prior education, and social interactions in improving completion rates.

Keywords: MOOCs, Machine Learning, Dropout Prediction, Course Completion, Engagement

Paper Id: 231736

Published On: 2020-04-04

Published In: Volume 8, Issue 2, March-April 2020

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