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Hutt, Stephen; Das, Sanchari; Baker, Ryan S. – International Educational Data Mining Society, 2023
The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…
Descriptors: Data Analysis, Decision Making, Data Collection, Foreign Countries
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Fancsali, Stephen E.; Murphy, April; Ritter, Steve – International Educational Data Mining Society, 2022
Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…
Descriptors: Learning Analytics, Educational Technology, Barriers, Data Analysis
Lewkow, Nicholas; Zimmerman, Neil; Riedesel, Mark; Essa, Alfred – International Educational Data Mining Society, 2015
Next generation digital learning environments require delivering "just-in-time feedback" to learners and those who support them. Unlike traditional business intelligence environments, streaming data requires resilient infrastructure that can move data at scale from heterogeneous data sources, process the data quickly for use across…
Descriptors: Electronic Learning, Data Analysis, Higher Education, Elementary Secondary Education
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Wu, Mike; Davis, Richard L.; Domingue, Benjamin W.; Piech, Chris; Goodman, Noah – International Educational Data Mining Society, 2020
Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing public policy. Yet larger…
Descriptors: Item Response Theory, Accuracy, Data Analysis, Public Policy
Zafra, Amelia; Ventura, Sebastian – International Working Group on Educational Data Mining, 2009
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Descriptors: Foreign Countries, Programming, Academic Achievement, Grades (Scholastic)