A generalized classifier to identify online learning tool disengagement at scale

Abstract

Student success, a major focus in higher education, in part, requires students to remain actively engaged in the required coursework. Identifying student disengagement, when a student stops completing coursework, at scale has been a continuing challenge for higher education due to the heterogeneity of traditional college courses. This research uses data from Connect by McGraw-Hill Education, a widely used online learning tool, to build a classifier to identify learning tool disengagement at scale. This classifier was trained and tested on four years of historical data, representing 4.5 million students in 175,000 courses, across 256 disciplines. Results show that the classifier is effective in identifying disengagement within the online learning tool against baselines, across time, and within and across disciplines. The classifier was also effective in identifying students at risk of disengaging from Connect and then earning unsuccessful grades in a pilot course for which the assignments in Connect were worth a relatively small portion of the overall course grade. Because Connect is widely used, this classifier is positioned to be a good tool for instructors and institutions to identify students at risk for disengagement from coursework. Instructors and institutions can use this information to design and implement interventions to improve engagement and improve student success at the institution in key courses.