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Investigating Gender Difference on Homework in Middle School Mathematics.

, , , and . EDM, page 364-369. International Educational Data Mining Society (IEDMS), (2016)

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Robust distributed estimation based on a generalized correntropy logarithmic difference algorithm over wireless sensor networks., , , , and . Signal Process., (2020)Informing Teachers Live about Student Learning: Reporting in Assistment System, and . Workshop on Usage Analysis in Learning Systems, Proceedings of Artificial Intelligence in Education, Amsterdam, (2005)Predicting State Test Scores Better with Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required., , and . Intelligent Tutoring Systems, volume 4053 of Lecture Notes in Computer Science, page 31-40. Springer, (2006)Using Data Mining Findings to Aid Searching for Better Cognitive Models., , and . Intelligent Tutoring Systems (2), volume 6095 of Lecture Notes in Computer Science, page 312-314. Springer, (2010)Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effects Methods., , , and . AIED, volume 158 of Frontiers in Artificial Intelligence and Applications, page 626-628. IOS Press, (2007)Teaching With a Fully Digital, Year-long Math Program: Learning Sciences Futures on the Front Line., , , , and . ICLS, International Society of the Learning Sciences, (2018)Identifying Gaps in Use of and Research on Adaptive Learning Systems., , , , , , , , and . CSEDU (1), page 118-124. SCITEPRESS, (2020)Predicting Students' Standardized Test Scores Using Online Homework., and . L@S, page 213-216. ACM, (2016)Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)?, and . EDM, page 41-50. www.educationaldatamining.org, (2010)Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System., , , and . IEEE Trans. Learn. Technol., 2 (2): 79-92 (2009)