Al Yusuf, H., & Heileman, G. L., & Islam, R., & Zhang, Y., & Agrawal, T., & Free, H. W., & Manasil, K. A. (2023, June), Progress Analytics in Support of Engineering Advising and Program Reform  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43967

\bibitem{asee_peer_43967}
  Al Yusuf, H., \& Heileman, G. L., \& Islam, R., \& Zhang, Y., \& Agrawal, T., \& Free, H. W., \& Manasil, K. A. (2023, June), \emph{Progress Analytics in Support of Engineering Advising and Program Reform}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43967

Husain Al Yusuf, Gregory L. Heileman, Raian Islam, Yiming Zhang, Tanmay Agrawal, Hayden William Free, and Kristina A. Manasil.  "Progress Analytics in Support of Engineering Advising and Program Reform".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://strategy.asee.org/43967 Internet.  18 Jul, 2024

\bibitem{asee_peer_43967}
  Husain Al Yusuf, Gregory L. Heileman, Raian Islam, Yiming Zhang, Tanmay Agrawal, Hayden William Free, and Kristina A. Manasil.
  "Progress Analytics in Support of Engineering Advising and Program Reform".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://strategy.asee.org/43967 Internet.  18 Jul, 2024

@INPROCEEDINGS{asee_peer_43967
author = "Husain Al Yusuf, Gregory L. Heileman, Raian Islam, Yiming Zhang, Tanmay Agrawal, Hayden William Free, and Kristina A. Manasil"
title = "Progress Analytics in Support of Engineering Advising and Program Reform"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/43967}
number = {10.18260/1-2--43967}
}

TY  - CPAPER
AB  - 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.
AU  - Husain Al Yusuf
AU  - Gregory L. Heileman
AU  - Raian Islam
AU  - Yiming Zhang
AU  - Tanmay Agrawal
AU  - Hayden William Free
AU  - Kristina A. Manasil
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Progress Analytics in Support of Engineering Advising and Program Reform
UR  - https://strategy.asee.org/43967
DO  - 10.18260/1-2--43967
ER  -