Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…

Dual enrollment (DE)--where students earn college credits during high school--is expanding rapidly. To facilitate DE, institutional actors across K-12 schools and colleges must build or repurpose structures across separate organizations to determine course offerings, assignments, modality, and composition. Yet the organization and implications of…

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…