Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models

Overview of attention for article published in International Journal of Educational Technology in Higher Education, September 2022

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So far, Dimensions has found 10 publications that cite this research output.

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Enhancing Predictive Performance in Identifying At-Risk Students: Integration of Topological Features, Node Embeddings in Machine Learning Models

Book chapter (February 2024)

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Article in IEEE Access (January 2024)

E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors

Article in Education Sciences (November 2023)