A Purdue #Learning Analytics #studentsuccess Experiment

Learning Analytics include a near-real time or real-time analysis of students’ performance yields actionable information resulting in meaningful student feedback such as where and when to get help. In 2012, Arnold & Pistilli examine Course Signals an early intervention system that uses a student success algorithm to predict which students that might be falling behind. The algorithm utilizes current course performance (percent of points earned in a course to date); effort (interaction with Blackboard Vista, Purdue’s Learning Management System); prior academic history including academic preparation, high school GPA, and standardized test scores; and student characteristics (exact algorithm is proprietary). The faculty member sends the results of the on-demand evaluation to each student along with a visual indicator (a stoplight traffic signal) depicting how each student is doing.

Arnold, K. E., & Pistilli, M. D. (2012, April). Course Signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270). ACM. Retrieved from:

http://www.itap.purdue.edu/learning/docs/research/Arnold_Pistilli-Purdue_University_Course_Signals-2012.pdf
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