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We found that dropout prediction fortunately works almost equally on groups induced by various grouping factors Dempsey, alexander moreno, christy k However, in the case of stem majors, the predictor collection can vary as a function of the targeted group.
Drawing on twelve years of administrative data at a large public university in the us, we find that dropout prediction at the end of the second year has a 20% higher auc than at the time of enrollment in a random forest model. Selected works in the context of distance education have made use of these opportunities in predictive modeling. 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.
Our data included complete lms records of all courses taught at uc berkeley between spring 2017 and spring 2021 Prior studies on mooc dropout prediction have encountered several challenges and limitations We consider all students in higher education in denmark who applied to higher education through the danish centralized system between 2006 and 2017 and have finished their enrollment, resulting either in graduation or dropout. 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]
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