Modeling decisions regarding the time of prediction and potentially different dropout mechanisms across student subgroups have to be better understood to build robust and reliable prediction systems.

We invite research and practice papers that address the “convergence of communities” in LAK and bring a novel perspective and approach for reflecting on the field. This theme is reflected in the workshops, …

Dropout prediction in MOOCs is a well-researched problem where we classify which students are likely to persist or drop out of a course. Most research into creating models which can predict outcomes is …

Mar 18, 2024 · Modeling decisions regarding the time of prediction and potentially diferent dropout mechanisms across student subgroups have to be better understood to build robust and reliable …

We proposed a student's dropout prediction model using an intuitionistic fuzzy set and an XGBoost algorithm called STOU2PM. The system that collected student datasets from 2012 to 2022 consisted …

Prior studies on MOOC dropout prediction have encountered several challenges and limitations. First, these studies often relied on complex feature extraction processes, making it dificult to gen-eralize …

To answer RQ1, we compare the prediction performance of the including approach and the excluding approach when predicting which students are likely to drop out.

This raises an important question: Does the fairness of dropout predictions remain consistent over time, or does it change as students advance through their studies?

In this study, we produce and evaluate the first machine-learned predictions of student course load ratings and gen- eralize our model to the full 10,000 course catalog of a large public university.

In this article, we study quantification of model uncertainty based on Monte-Carlo Dropout (MC Dropout) neural networks in prediction models developed from CPS data.