Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics

In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students’ changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the organization of the curriculum) that highlight different trends in student experience of the focal course. Course difficulty, discipline of major, time in semester, and simultaneous difficulty across courses were all significantly related to entering a moderate and high-risk classification in the early warning system (EWS). Course difficulty, discipline of major, and time in semester were related to exiting academic difficulty classifications. We observe a snowball effect, whereby students who are experiencing difficulty in the focal course are at increased risk of experiencing difficulty in their other courses. Our findings suggest that different metrics may be needed to identify risk for academic difficulty and recovery from academic difficulty. Our results demonstrate what a more holistic assessment of academic functioning might look like in early warning systems and course recommender systems, and suggest that academic planners consider the relationship between course co-enrollment and student academic success.This article is published as Brown, M.G, DeMonbrun, R., and Teasley, S. Taken Together: Conceptualizing students concurrent course enrollment across the postsecondary curriculum using temporal analytics. Journal of Learning Analytics 5 (2018): 60-72. doi: 10.18608/jla.2018.53.5.</p

Conceptualizing co-enrollment: accounting for student experiences across the curriculum

In this study, we develop and test three measures for conceptualizing the potential impact of co-enrollment in different courses on students’ changing risk for academic difficulty in a focal course. Two of these measures, concurrent enrollment in at least one difficult course and academic difficulty in the prior week in courses other than the focal course, significantly increase students’ odds of academic difficulty in the focal course in our models. Our results have implications for the designs of Early Warning Systems and suggest that academic planners consider the relationship between course coenrollment and students’ academic success.This is a manuscript of a proceeding from Michael Brown, R. Matthew DeMonbrun, & Stephanie D. Teasley. 2018. Conceptualizing Co-enrollment: Accounting for student experiences across the curriculum. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, Sydney, Australia, March 7-9, 2018. ACM. doi: 10.1145/3170358.3170366. Posted with permission.</p