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Identifying Students' Mechanistic Explanations in Textual Responses to Science Questions with Association Rule Mining.

, , and . ICDM Workshops, page 264-268. IEEE Computer Society, (2015)

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Why Is Zero Marking Important in Korean?, , and . IJCNLP, volume 3651 of Lecture Notes in Computer Science, page 588-599. Springer, (2005)Tracking student progress in a game-like learning environment with a Monte Carlo Bayesian knowledge tracing model., , , , , and . LAK, page 166-170. ACM, (2015)The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing., , and . Comput. Educ., (2020)Appropriating a Climate Science Discourse About Uncertainty in Online Lessons., , and . CSCL, International Society of the Learning Sciences, (2017)Identifying Students' Mechanistic Explanations in Textual Responses to Science Questions with Association Rule Mining., , and . ICDM Workshops, page 264-268. IEEE Computer Society, (2015)Using automatic image processing to analyze visual artifacts created by students in scientific argumentation., , and . Br. J. Educ. Technol., 50 (6): 3391-3404 (2019)Detecting and Correcting Learner Korean Particle Omission Errors., , and . IJCNLP, page 1419-1427. Asian Federation of Natural Language Processing / ACL, (2013)Developing Learner Corpus Annotation for Korean Particle Errors., , and . LAW@ACL, page 129-133. The Association for Computer Linguistics, (2012)Developing Methodology for Korean Particle Error Detection., , and . BEA@ACL, page 81-86. Association for Computational Linguistics, (2011)Scaffolding a Lesson With Noisy Data: One Physics Lesson, Two Teacher Approaches., , , and . ICLS, International Society of the Learning Sciences, (2020)