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General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge.

, , and . EDM, page 84-91. International Educational Data Mining Society (IEDMS), (2014)

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Which System Differences Matter? Using L1/L2 Regularization to Compare Dialogue Systems., and . SIGDIAL Conference, page 8-17. The Association for Computer Linguistics, (2011)How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences., , and . EDM, page 169-178. www.educationaldatamining.org, (2011)A Framework for Multifaceted Evaluation of Student Models., , , and . EDM, page 203-210. International Educational Data Mining Society (IEDMS), (2015)Inferring Course Enrollment from Partial Data., and . AIED (2), volume 10948 of Lecture Notes in Computer Science, page 429-432. Springer, (2018)Mining the Web to Leverage Collective Intelligence and Learn Student Preferences., , and . EDM (Workshops), volume 1183 of CEUR Workshop Proceedings, CEUR-WS.org, (2014)Beyond Word Embeddings: Dense Representations for Multi-Modal Data., , and . FLAIRS, page 149-154. AAAI Press, (2019)Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models., and . EDM, page 49-56. www.educationaldatamining.org, (2012)Modeling Skill Acquisition Over Time with Sequence and Topic Modeling.. AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)Joint Discovery of Skill Prerequisite Graphs and Student Models., , and . EDM, page 46-53. International Educational Data Mining Society (IEDMS), (2016)Predicting Task Completion from Rich but Scarce Data., and . EDM, page 291-292. www.educationaldatamining.org, (2010)