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Stephanie J. Blackmon; Robert L. Moore – Journal of Computing in Higher Education, 2024
As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…
Descriptors: Learning Analytics, Networks, Models, Ethics
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Hanqiang Liu; Xiao Chen; Feng Zhao – Education and Information Technologies, 2024
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…
Descriptors: MOOCs, Video Technology, Behavior Patterns, Prediction
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Marcel R. Haas; Colin Caprani; Benji T. van Beurden – Journal of Learning Analytics, 2023
We present an innovative modelling technique that simultaneously constrains student performance, course difficulty, and the sensitivity with which a course can differentiate between students by means of grades. Grade lists are the only necessary ingredient. Networks of courses will be constructed where the edges are populations of students that…
Descriptors: Bayesian Statistics, Computer Software, Learning Analytics, Grades (Scholastic)
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Godwin-Jones, Robert – Language Learning & Technology, 2021
Data collection and analysis is nothing new in computer-assisted language learning, but with the phenomenon of massive sets of human language collected into corpora, and especially integrated into systems driven by artificial intelligence, new opportunities have arisen for language teaching and learning. We are now seeing powerful artificial…
Descriptors: Data Collection, Academic Achievement, Learning Analytics, Computer Assisted Instruction