2025 ASEE Annual Conference & Exposition

An enormous amount of data is generated daily by educational tools used in traditional (e.g., colleges) and non-traditional (e.g., open online course provider; Coursera) learning environments. This data holds the potential to uncover valuable insights that can inform instructional decision-making. Despite the widespread adoption of data mining techniques in education, these methods often seem like a “black box” limiting educators’ ability to interpret their decision-making and outputs. To address this challenge, studies have explored the use of Explainable Artificial Intelligence (xAI) to improve the transparency and interpretation of data mining algorithms in educational settings. This work-in-progress study employs several data mining algorithms to extract critical insights from the publicly available extensive educational datasets. These datasets contain information on students’ perceived health status and its relation with their mental health, academic performance in college, and dropout rates. We then applied an xAI technique, i.e., SHAP (Shapley Additive explanations), to interpret and better understand the output of these algorithms. Preliminary findings reveal that the applied data mining algorithms, combined with xAI techniques, were able to identify key factors contributing to student academic performance, identify the early warning signs of mental or physical health concerns, and assess students at risk of dropping out. This work contributes to the current educational data mining literature by offering guidelines for integrating xAI methods with data mining algorithms to enhance their interpretability in educational contexts. The future direction of this work is to explore other xAI techniques and apply them to diverse educational datasets. Furthermore, future research is also needed to evaluate the impact of this combined approach (data mining model + xAI) on decision-making in practical educational settings.