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Using natural language processing tools to develop complex models of student engagement.

, , , , , and . ACII, page 542-547. IEEE Computer Society, (2017)

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Unifying Computer-Based Assessment Across Conceptual Instruction, Problem-Solving, and Digital Games., , and . Technol. Knowl. Learn., 19 (1-2): 165-181 (2014)Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research., , , and . Int. J. Artif. Intell. Educ., 28 (2): 139-151 (2018)Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics., , , and . Int. J. Artif. Intell. Educ., 32 (1): 174-195 (2022)Comparing machine learning to knowledge engineering for student behavior modeling: a case study in gaming the system., and . Interact. Learn. Environ., 27 (5-6): 585-597 (2019)Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis., , , , and . CSCL, International Society of the Learning Sciences, (2017)Improving Engagement in an E-Learning Environment., , , , and . AIED, volume 9112 of Lecture Notes in Computer Science, page 730-733. Springer, (2015)An architecture for identifying and using effective learning behavior to help students manage learning., , , , , and . AIED Workshops, volume 1009 of CEUR Workshop Proceedings, CEUR-WS.org, (2013)Using a Model for Learning and Memory to Simulate Learner Response in Spaced Practice., , , , and . AIED, volume 10331 of Lecture Notes in Computer Science, page 644-649. Springer, (2017)Reengineering the Feature Distillation Process: A case study in detection of Gaming the System., , , and . EDM, page 284-287. International Educational Data Mining Society (IEDMS), (2014)The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students., , , , and . CoRR, (2019)