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Direct linkBui, Ngoc Van P. – ProQuest LLC, 2022
This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and…
Descriptors: Artificial Intelligence, At Risk Students, Prediction, Data Science
Sahar Voghoei – ProQuest LLC, 2021
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. Their objective is to provide timely information that may enable educators to channel the most effective remedial treatments towards precisely targeted students in an efficient manner. The present…
Descriptors: Data Science, Academic Achievement, School Holding Power, Predictor Variables
