A Machine Learning Approach to Predict Learner Dropout in Online Courses

While the development of Massive Open Online Courses (MOOCs) and other online learning systems have changed the way education is delivered, dropout rates continue to be a challenge. This study suggests a behavioral and participation pattern predictive model for dropout using machine learning algorithms. The analysis focuses on a dataset from a popular MOOC, which has key features like clickstream data, forum participation, assignment submissions, and time spent on course videos. Supervised learning algorithms such as Random Forest, Logistic Regression, Support Vector Machine (SVM), and Gradient Boosting were trained and evaluated. The highest performance was from the Random Forest classifier with an F1 of 0.89 and AUC of 0.93. This study also evaluates the feature sets and models to find the most accurate predictors of dropout and measures the predictive power of diversified feature sets. These results demonstrate that there is opportunity to apply machine learning techniques to unstructured data in order to flag students likely to drop out prior to course completion, enabling timely assistance which has demonstrated redemption potential for retention. The increasing amount of available data regarding student interaction with online learning environments fueled developments in educational data mining, which the study now adds to along with practical value for educators, administrators, and developers striving to enhance learner achievement in digital educational settings.