Using Cohort-Based Analytics to Better Understand Student Progress

Engineering programs are typically among the most tightly prescribed programs within the academic landscape on any university campus. The strict nature of these programs often results in students taking more credits than stipulated, thereby leaving them struggling to graduate in a timely manner. The ability to identify potential blockers or challenges in an engineering program's curriculum is vital to student success and the promotion of on-time graduation. This paper provides a comprehensive examination of patterns and trends observed by a newly developed cohort tracking analytics platform. This platform provides analyses over a cohort of students which uncovers insights that are not easily identified when only looking at data at the individual student level. The analysis pinpoints courses that many students within the cohort have taken that are not applicable to the degree, along with the reasons why these courses are not applicable. It also identifies trends in courses that must be repeated by a significant portion of the cohort. It examines the courses constituting a program's degree requirements that have yielded both the best and worst grade value outcomes. In addition, an exploration of a cohort's efficiency of credit hour production is provided for both the home institution units and transfer units, which shows where credits are not aligning with degree requirements and therefore not counting towards degree completion. Finally, a comparative analysis of programs within the engineering field is performed as well as a comparison of engineering programs to non-engineering programs. This type of analysis demonstrates the differences in how students in engineering programs make progress towards their degree completion. The statistical analyses furnished by this platform provide administrators with an evidence-based foundation to support programmatic modifications and enhancements. This allows administrators to depart from the past practices of having to rely on anecdotal evidence and individual experiences. The empirical information from this platform assists advisors in aiding students in creating academic plans that provide students with the best chance for success while maximizing their credit hour efficiency. In this paper, the architecture and the visual display of the cohort tracking analytics platform are briefly discussed. Then we pivot to focus on the results of the analyses, comparing and contrasting three groups that consist of engineering disciplines within a department, departments within engineering colleges, and engineering colleges to other colleges at the institution. We conclude with a discussion of the potential actionable changes dictated by these results.