Ecadf

ecadf represents a topic that has garnered significant attention and interest. Temporal and Between-Group Variability in College Dropout Prediction. 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 function of the targeted group. Predictive Modeling of Student Dropout Using Intuitionistic Fuzzy Sets ....

In this context, 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 of approximately 268,763 instances and was prepared to build an accurate prediction model. Fairness Over Time: A Nationwide Study of Evolving Bias in Dropout .... In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. MOOC Dropout Prediction - ACM Digital Library.

Additionally, we demonstrate how existing MOOC dropout prediction pipelines can be made interpretable, all while having predictive performance close to existing tech-niques. We explore each stage of the pipeline as design com-ponents in the context of interpretability. Methodological Considerations for Predicting At-risk Students. 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.

SIG-Net: GNN based dropout prediction in MOOCs using Student .... Prior studies on MOOC dropout prediction have encountered several challenges and limitations. In this context, first, these studies often relied on complex feature extraction processes, making it dificult to gen-eralize the methods across diferent datasets.

Insights into undergraduate pathways using course load analytics. 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]. Selected works in the context of distance education have made use of these opportunities in predictive modeling. Educational Data Mining-based visualization and early prediction of ....

Additionally, dropout prediction in e-learning courses through the combination of machine learning techniques. In relation to this, computers & Education, 53, 3 (November 2009), 950–965. https://doi.org/10.1016/j.compedu.2009.05.010 What You Do Predicts How You Do - ACM Digital Library. Another key aspect involves, mOOC dropout prediction using machine learning techniques: review and research challenges.

2018 IEEE Global Engineering Education Conference (EDUCON) (2018), 1007–1014. Difficult Lessons on Social Prediction from Wisconsin Public Schools. Keywords Resource Allocation, Dropout Prediction, AI in Education ACM Reference Format: Juan Carlos Perdomo, Tolani Britton, Moritz Hardt, and Rediet Abebe. In The 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25), June 23–26, 2025, Athens ...