Predicting student progress, optimizing classes schedules

Predicting any kind of human behaviour is a difficult task, predicting student progress in online/offline courses is no exception. Our client, an international language school, wanted to improve scheduling of their offline classes by predicting students’ progress in the online part of the course.

Exploiting the power of EventAI in transforming event-based data into a meaningful machine-learning algorithm input, we were able to predict finishing times of course’s online chunks for a given student. EventAI, with the ease of declaring features and aggregation functions, made the feature engineering process simpler, less prone to errors and easy to control. Having the predictions, we were able to optimise offline classes schedule, improve classes occupancies and reduce students waiting time.

Industry: Education
Technologies: EventAI | Python | Scala

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