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How Close are Predictive Models to Teachers in Detecting Learners at Risk?

, , , and . UMAP, page 135-145. ACM, (2023)

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International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)., , , and . ECIR (2), volume 12036 of Lecture Notes in Computer Science, page 637-640. Springer, (2020)Equality of Learning Opportunity in Personalized Recommendations., , , and . CoRR, (2020)Causal reasoning for algorithmic fairness in voice controlled cyber-physical systems., , , and . Pattern Recognit. Lett., (April 2023)Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality., , , and . CoRR, (2022)Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning., , , and . CoRR, (2022)Improving Fairness in Speaker Recognition., , , and . CoRR, (2021)Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms., , , and . AIED (1), volume 13355 of Lecture Notes in Computer Science, page 90-102. Springer, (2022)FATED 2022: Fairness, Accountability, and Transparency in Educational Data., , , , , , and . EDM, International Educational Data Mining Society, (2022)Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs., , , , and . EDM, International Educational Data Mining Society, (2022)Evaluation Framework for Context-aware Speaker Recognition in Noisy Smart Living Environments., , and . CIKM (Workshops), volume 2699 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)