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Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika – Journal of Computing in Higher Education, 2022
Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In…
Descriptors: College Faculty, Teacher Attitudes, College Students, Privacy
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Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros – Journal of Computing in Higher Education, 2021
Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…
Descriptors: Active Learning, Predictor Variables, Academic Achievement, Learning Analytics
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Monllaó Olivé, David; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – Journal of Computing in Higher Education, 2020
Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments.…
Descriptors: Identification, At Risk Students, Potential Dropouts, Online Courses