Fairness Over Time: A Nation-wide Study of Evolving Bias in Dropout Prediction. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy.

We found that dropout prediction fortunately works almost equally on groups induced by various grouping factors. However, in the case of STEM majors, the predictor collection can vary as a …

To the best of our knowledge, there have been no existing graph-based studies specifically on the KDDCUP 2015 dataset for dropout prediction. Additionally, our study represents the first dropout …

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 …

In order to assess which features are most useful for dropout prediction, we examine the learned properties of the final models that are selected by the nested cross-validation process.

Mar 13, 2023 · Given that transfer students who entered UC Berkeley in Fall 2017 nominally are in their fifth semester, we compared those students to their non-transfer peers from the Fall 2015 intake.

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 …

Furthermore, we analyze real-world data from a course dropout prediction model to answer RQ2. Specifically, we use Newcombe’s Hybrid score method and bootstrapping to con-struct confidence …

replication protocols, like Paxos, for fault tolerance. In this paper, we present a new approach that makes transactional storage systems more affordable: we eliminate consistency from the replication …