Ilmu Komputer & AI editorial
Open AccessOA2026
Automating Freshman Course Placement and Registration: A Case Study
A cross-departmental automation at Rowan University processes 3,500+ incoming students and saves over 350 staff hours annually
Bharathwaj Vijayakumar; Samyukta Alapati; Sahana Varadaraju· 2026· DOI 10.48550/arXiv.2608.08776
The core problem
Rowan University's Freshman Instructional Guides (FIGS) process was historically executed manually, requiring substantial time from Testing Services, University Advising, and the Registrar's Office to evaluate placement needs and assign students to courses. Over a decade, first-time degree-seeking student enrollment surged by 57%, rendering the manual processes increasingly unsustainable. This implementation report explores the cross-departmental effort to automate freshman course placement and registration, addressing the scalability challenges posed by growing enrollment. The core problem centered on the inefficiency and error potential of manual placement, which consumed significant staff resources and delayed student registration. The authors—Bharathwaj Vijayakumar, Samyukta Alapati, and Sahana Varadaraju—outline the context, design architecture, technical integration, assessment methods, lessons learned, and practical implications for institutions facing similar challenges.
Innovation
The automated system successfully processed over 3,500 incoming students, achieving more than 350 hours in annual time savings. This represents a significant reduction in administrative workload for Testing Services, University Advising, and the Registrar's Office. The automation also reduced the potential for human error inherent in manual placement and registration. Staff were enabled to shift focus from repetitive administrative tasks to strategic advising, enhancing student support. The time savings can be quantified as:
Given the 57% surge in first-time degree-seeking student enrollment over a decade, the automation provided a scalable solution to accommodate growth without proportional increases in staff time. The system's ability to check real-time availability and constraints in Banner ensured accurate and timely registration for freshmen.
Rowan University's Freshman Instructional Guides (FIGS) process was historically executed manually, requiring substantial time from Testing Services, University Advising, and the Registrar's Office to evaluate placement needs and assign students to courses. Over a decade, first-time degree-seeking student enrollment surged by 57%, rendering the manual processes increasingly unsustainable. This implementation report explores the cross-departmental effort to automate freshman course placement and registration, addressing the scalability challenges posed by growing enrollment. The core problem centered on the inefficiency and error potential of manual placement, which consumed significant staff resources and delayed student registration. The authors—Bharathwaj Vijayakumar, Samyukta Alapati, and Sahana Varadaraju—outline the context, design architecture, technical integration, assessment methods, lessons learned, and practical implications for institutions facing similar challenges.
The automation initiative was developed by a cross-departmental team that integrated data from multiple sources: Banner (the Student Information System), Google Sheets maintained by Advising, and other institutional data. The system classifies students based on program groupings, determines primary and secondary course placements, checks for real-time availability and constraints in Banner, and completes course registration for freshmen in bulk. The technical architecture likely follows a pipeline: data ingestion from Banner and Google Sheets, student classification, placement determination, availability checking, and bulk registration. A Mermaid diagram illustrating this flow is provided below.
Why it matters
The Rowan University case demonstrates the feasibility and benefits of automating complex, cross-departmental processes in higher education. Key lessons learned include the importance of cross-departmental collaboration, the need for robust data integration across systems like Banner and Google Sheets, and the value of real-time constraint checking to avoid registration conflicts. The automation not only addressed scalability but also improved accuracy and freed staff for higher-value activities. Practical implications for other institutions include the potential to replicate this model, though challenges such as data consistency, system interoperability, and change management must be considered. The authors suggest that similar automation can be adapted by institutions facing comparable enrollment pressures and manual process bottlenecks. The taxonomy candidates (Architecture, Cybersecurity, Network, Cryptography) are not directly addressed in the source, but the system architecture and data integration aspects align with architectural considerations.
Who should read this
CS practitioners and researchers
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