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Challenges of Using Observational Data to Determine the Importance of Example Usage

, , and . page 633--637. Springer International Publishing, Berlin, (2015)
DOI: 10.1007/978-3-319-19773-9_79

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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)A Framework for Multifaceted Evaluation of Student Models., , , and . EDM, page 203-210. International Educational Data Mining Society (IEDMS), (2015)How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences., , and . EDM, page 169-178. www.educationaldatamining.org, (2011)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)Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models., and . EDM, page 49-56. www.educationaldatamining.org, (2012)Classifying dialogue in high-dimensional space., and . ACM Trans. Speech Lang. Process., 7 (3): 8:1-8:15 (2011)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)