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The Role of Temporal Patterns in Students' Behavior for Predicting Course Performance: A Comparison of Two Blended Learning Courses
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van Leeuwen, Anouschka; Bos, Nynke; van Ravenswaaij, Heleen; van Oostenrijk, Jurgen – British Journal of Educational Technology, 2019
In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a…
Descriptors: Higher Education, Blended Learning, Educational Technology, Technology Uses in Education
Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019)
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Proceedings of the Seventh International Conference on Educational Data Mining (EDM) (7th, London, United Kingdom, July 4-7, 2014)
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Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection