ROSI Change Advisory Board

The ROSI Change Advisory Board (CAB) is a body that establishes priorities and directives for implementing ROSI enhancements. This dynamic board was established in May 2016 on a model of collaborative governance. The 20 member committee is made up of tri-campus functional representatives (mostly central and cross-divisional associate registrars) and technical leads and managers from EASI.

Members of the ROSI CAB are very familiar with the platform on which U of T’s institutional and student information sits. They are also mindful of trends in higher education as they affect records and registration systems, and constraints of the current ROSI system.

Since its inception, this board has reviewed and prioritized over 80 ROSI Enhancement Requests received from 15 units across U of T. It has now become a model for other student information system applications.

Examples of Completed Enhancement Requests:

  • Refined fields in ROSI for error management, government reporting and interoperability with other systems.
  • Blocked students from removing themselves from courses marked Grade Withheld Pending Review.
  • Disabled UTORid for expelled students.
  • Created a batch upload to add transcript notations for award citations.
  • Limited students’ maximum course load per term (versus academic session).
  • Linked lectures with specific tutorials in ACORN.
  • Enhanced data extracts to support divisional needs.

Examples of Enhancement Requests Under Consideration:

  • Integrate data and procedures between ROSI and the TCard Office to better manage student names and immigration status.
  • Improve management of ‘repeatable’ courses (e.g., selected or special topics courses) allowing students to enrol on ACORN.

Examples of Ongoing or Approved Requests:

Ministry of Training, Colleges and Universities Support
Continue to support the ROSI MTCU Extract and Submission process.

OUAC Maintenance
Continue to analyze the impact to the ROSI admission process and make changes based on requests received from the Ontario Universities’ Application Centre (OUAC).

ROSI – Slate Integration
Analysis of enhancements for integration of Slate, the new Enrolment Services admissions tool, with ROSI to improve ongoing operations.

Refactoring ROSI Course Instructor Assignments
(Summer 2019)
Investigation of solutions to better manage identification of faculty and staff from the University and the Federated Colleges in order to assign instructor and coordinator roles in courses for ROSI and the CIS.

Completed Projects

Glassfish 2 – JAVA 6 Updates
(Complete – Summer 2018)
Updates to ROSI Express (RXP) to migrate from the existing infrastructure (Glassfish 2) to a newer, more modern platform (IBM). This new version of RXP will initially be delivered to run in parallel to the current production system. This is an essential pre-requisite for the NGSIS Platform Modernization project.

ACORN Functional & Technical Debt
(Complete – Fall 2019)
Work to address various updates requested by stakeholder groups, as well as re-writing code to address deficiencies, and add more comprehensive test cases, etc.

Operational and Administrative Reporting
(Partially Complete – 2018-19)
Reviewed options for a new reporting tool to allow for the development of user-friendly online reports for the student information system.

ROSI Performance Enhancements
In preparation for the Faculty of Arts & Science “priority drop” enrolment period, the peak enrolment day for U of T, the NGSIS team implemented low-investment, high value improvements to ACORN. Improvements included optimizing Weblogin to better handle the large volume of login requests, as well as a ‘webload management day’ waiting page where if students tried to log in to ACORN before their scheduled start time, their session would be kept active and prevent Weblogin from having to process repeated login attempts. This further improved the system’s overall performance.

The team also increased the duration and volume of cached registration information in the system to improve performance. If an individual student made an unusually rapid number of requests, they would automatically be prompted to prove that they were a human via a “captcha.”