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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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Supporting Instructors with Course Attendance and Quality Prediction in Synchronous Learning., , , and . HELMeTO, volume 1779 of Communications in Computer and Information Science, page 71-83. Springer, (2022)Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education., , and . AIED (1), volume 13355 of Lecture Notes in Computer Science, page 243-255. Springer, (2022)How Close are Predictive Models to Teachers in Detecting Learners at Risk?, , , and . UMAP, page 135-145. ACM, (2023)Modelling Student Behavior in Synchronous Online Learning during the COVID-19 Pandemic., and . L2D@WSDM, volume 2876 of CEUR Workshop Proceedings, page 28-40. CEUR-WS.org, (2021)Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning., , , and . LAK, page 441-452. ACM, (2023)Applying the ETL Process to Blockchain Data. Prospect and Findings., , , , and . Inf., 11 (4): 204 (2020)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)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)