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Exploring college major choice and middle school student behavior, affect and learning: what happens to students who game the system?

, , , and . LAK, page 36-40. ACM, (2015)

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The eXtensible Tutor Architecture: A New Foundation for ITS., , , and . AIED, volume 125 of Frontiers in Artificial Intelligence and Applications, page 902-904. IOS Press, (2005)A Quasi-Experimental Evaluation of An On-Line Formative Assessment and Tutoring System, , and . Journal of Educational Computing Research, 43 (4): 489--510 (Dec 1, 2010)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)The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Content Creation., , , , , , and . IEEE Trans. Learn. Technol., 2 (2): 157-166 (2009)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)Discovering 'Tough Love' Interventions Despite Dropout., , , , and . EDM, page 650-651. International Educational Data Mining Society (IEDMS), (2016)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)