Capturing Fairness and Uncertainty in Student Dropout Prediction - A Comparison Study

This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural network (RNN), and tree-based algorithms. Unlike existing studies, we arguably fairly compare each algorithm with the dataset that it can perform best with, thus `like for like'. I.e., we use a time-series dataset `as is' with algorithms suited for time-series, as well as a conversion of the time-series into a discrete-variables dataset, through feature engineering, with algorithms handling well discrete variables. We show that these much lighter discrete models outperform the time-series models. Our work additionally shows the importance of handing the uncertainty in the data, via these `compressed' models.

Automated summary

This study aims to explore and improve ways of handling continuous variable dataset to predict student dropout in MOOCs, by implementing various models including RNN and tree-based algorithms. The research shows that using discrete variable models after converting time-series data through feature engineering outperforms time-series models, emphasizing the importance of handling uncertainty in data.