2023 ASEE Annual Conference & Exposition

Progress Analytics in Support of Engineering Advising and Program Reform

Presented at Engineering Programs and Institutional Factors

Students in engineering programs are typically among those having the highest time-to-degree for any of the programs offered on a university campus. Keeping a cohort of students on track to- wards on-time graduation is extremely difficult given the tightly prescribed nature of engineering programs. Any deviation from the standard degree plan, for any reason, including not passing a class, taking courses out of sequence, etc., often precludes the ability to graduate in four years. In this paper, we describe a cohort tracking analytics platform that can be used by advisors as an aid in keeping students on track, and by program administrators as a tool to better understand the cur- ricular impediments associated with delays in graduation. This cohort analytics platform provides analyses over a population of students, rather than individual students, yielding valuable (often hid- den) information regarding the impediments that students face. For instance, this platform makes it easier to determine what courses are most significant in blocking the progress of a cohort, the efficiency of credit hour production within a cohort, where students are losing credit hours (i.e., generating credit hours that do not count towards the satisfaction of any degree requirements), etc.
Advisors and administrators often suggest programmatic improvements based on anecdotal evidence and experiences related to individual students, not because they are lazy, but because it is inherently difficult to compute cumulative student progress over a cohort. The reason for this is that accurate student progress information is typically difficult to obtain and out of reach for many decision makers, as degree audit capabilities have not been designed with analytics in mind. In an attempt to make this data accessible and actionable, we have developed a platform that can organize student cohorts according to any criteria, and compute progress analytics relative to these cohorts, while also providing useful analytics and visualizations in an appealing and easy-to-understand format. At the core of the platform is a database that stores program degree requirements and student data, as well as a progress reasoner and a curricular analytics engine that can compute cohort-based metrics and display them on an interactive dashboard. The architecture of this plat- form will be described in this paper, as well as the types of data that must be collected in order to use this platform effectively. We will also discuss the characteristics of cohort-based analytics that have emerged from the study of engineering programs, and how they differ from those generated from non-engineering programs.

Authors

Husain Al Yusuf http://orcid.org/0000-0002-4769-2089 The University of Arizona [biography]

Husain Al Yusuf is a second year PhD student in the Electrical and Computer Engineering Department at the University of Arizona. He is currently pursuing his PhD with a research focus on higher education analytics, with the goal of improving student outcomes and enhancing the effectiveness of higher education institutions.

Husain Al Yusuf holds a M.Sc in Computer Engineering from the University of New Mexico and has over 10 years of professional working experience as a technology engineer
Prof. Gregory L. Heileman http://orcid.org/0000-0002-5221-5682 The University of Arizona [biography]

Gregory (Greg) L. Heileman currently serves as the Associate Vice Provost for Academic Administration and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating collaboration across campus t
Raian Islam The University of Arizona [biography]

Raian Islam is a current Master’s student and a Graduate Research Assistant in the Department of Electrical and Computer Engineering at The
University of Arizona, Tucson, AZ, USA. She received her BSc. degree in Electrical and Electronic Engineering from Ahsanullah University of
Science and Technology, Dhaka, Bangladesh, in 2019. Her current research interests include higher education data analytics, machine learning and photovoltaics.
Yiming Zhang The University of Arizona [biography]

Yiming Zhang is a Ph.D. candidate in the Electrical and Computer Engineering Department at the University of Arizona. His research interest includes machine learning, data analytics and optimization in curricular design.
Tanmay Agrawal [biography]

Tanmay is a senior undergraduate student majoring in Computer Science with a minor in Entrepreneurship and Economics and worked on the blockchain-based reward system as well as building dashboards to understand studen outcomes. Tanmay is originally from India, and enjoys learning new things. In addition to his technical pursuits, he has a keen interest in discovering novel ideas, especially related to the social and political dimensions of our everyday existence. He relishes examining the broader context of all things.
Mr. Hayden William Free Georgia Institute of Technology [biography]

Hayden Free is an Master's student studying Computer Science at the Georgia Institute of Technology. His focused area of interests include network science, machine learning, and software design.
Kristina A. Manasil The University of Arizona [biography]

Kristi Manasil is a first-year PhD student in the School of Information at the University of Arizona. She received her bachelor's degree in Computer Science from the University of Arizona. She is interested in data visualization, machine learning, human computer interaction, and learning analytics.

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