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ERIC Number: ED560553
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 6
Abstractor: As Provided
Reference Count: 19
Analyzing Early At-Risk Factors in Higher Education E-Learning Courses
Baker, Ryan S.; Lindrum, David; Lindrum, Mary Jane; Perkowski, David
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
College students enrolled in online courses lack many of the supports available to students in traditional face-to-face classes on a campus such as meeting the instructor, having a set class time, discussing topics in-person during class, meeting peers and having the option to speak with them outside of class, being able to visit faculty during office hours, and so on. Instructors also lack these interactions, which typically provide meaningful indications of how students are doing individually and as a cohort. Further, online instructors typically carry a heavier teaching load, making it even more important for them to find quick, reliable, and easily understandable indicators of student progress, so that they can prioritize their interventions based on which students are most in need. In this paper, we study very early predictors of student success and failure. Our data is based on student activity, and is drawn from courses offered online by a large private university. Our data source is the Soomo Learning Environment, which hosts the course content as well as extensive formative assessment. We find that students who access the resources early, continue accessing the resources throughout the early weeks of the course, and perform well on formative activities are more likely to succeed. Through use of these indicators in early weeks, it is possible to derive actionable, understandable, and reasonably reliable predictions of student success and failure. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society