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A Bandit You Can Trust

, , and . Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, ACM, (June 2023)
DOI: 10.1145/3565472.3592955

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Exploring Common Trends in Online Educational Experiments., , , , , and . EDM, International Educational Data Mining Society, (2022)Lessons Learned from Scaling Up a Web-Based Intelligent Tutoring System, , , and . (2008)The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment., , , , , and . LAK, page 279-288. ACM, (2016)Incorporating Rich Features into Deep Knowledge Tracing, , , , and . Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, page 169--172. New York, NY, USA, ACM, (2017)Representing Student Performance with Partial Credit., , and . EDM, page 335-336. www.educationaldatamining.org, (2010)Using multiple Dirichlet distributions to improve parameter plausibility., , and . EDM, page 61-70. www.educationaldatamining.org, (2010)Can we predict which groups of questions students will learn from?., , , and . EDM, page 218-225. www.educationaldatamining.org, (2008)The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS., , , and . EDM, page 147-156. www.educationaldatamining.org, (2008)Clustering Students in ASSISTments: Exploring System- and School-Level Traits to Advance Personalization., , , and . EDM, International Educational Data Mining Society (IEDMS), (2017)Do students really learn an equal amount independent of whether they get an item correct or wrong?, , , and . EDM, page 304-305. International Educational Data Mining Society, (2013)