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Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – AERA Open, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Identification, Two Year College Students, Community Colleges
Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Higher Education, Predictive Measurement, Models
Yen, Cherng-Jyh; Abdous, M'hammed – International Journal of Distance Education Technologies, 2011
The confluence of technology convergence, market forces, and student demand for greater access is reshaping higher education institutions. Indeed, the convergence of technological innovations in hardware, software, and telecommunications, combined with the ubiquity of learning management systems, is reconfiguring and strengthening traditional…
Descriptors: Learner Engagement, Blended Learning, Distance Education, Educational Technology

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