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 …

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 …

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 …

In this paper, we investigate the issue of using protected at- tributes in college dropout prediction in real-world contexts. Protected attributes are traits or characteristics based on which …

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 …

Dropout prediction in higher education is a widely studied task [1], including many studies that examine the algorithmic fairness of these prediction models [7, 9].

Mar 13, 2023 · While multiple studies have presented viable models of higher education dropout prediction [2, 33], these models left room for improvement or, at least, exploration [24]. …

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.

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 …

Writing-to-learn pedagogies are an evidence-based practice known to aid students in constructing knowledge. Barriers exist for the implementation of such assignments; namely, instructors feel …