Welcome to The University of Toronto Learning Analytics Site
Learning Analytics at U of T
What is Learning Analytics?
A commonly used definition of Learning Analytics was developed by the Society for Learning Analytics Research:
“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
Project Background
There is growing interest across the University of Toronto (U of T) community in using data to inform and enhance activities related to the teaching and learning mission of the university. These include pedagogical enhancement and innovation within course and program contexts, academic success support at the program and degree levels, potential tools and resources that may support students in their learning journey, and strategic decision-making for the institution.
In 2021 the Office of the Vice-Provost, Innovations in Undergraduate Education (OVPIUE) initiated a consultation process which explored how these goals can be met at UofT and peer institutions through the intersection of data, human-centered design, and student learning. This resulted in a set of recommendations published in a Learning Analytics Strategy Paper.
Community input emphasized that the overarching purpose of work in this domain must be to benefit student learning and success. The goals driving the development of the U of T Learning Analytics Initiative, as outlined in the Strategy Paper, are as follows:
- Improving student learning experiences and outcomes
- Empowering students to plan and manage their learning paths
- Enabling instructors and staff to access and leverage learning data in support of teaching and learning activities
- Optimizing structure and support of digital learning environment
Current Learning Analytics Initiatives
The Learning Analytics Initiative at the University of Toronto is focused on supporting instructors in improving course and curriculum design. Initial projects include both building technology infrastructure in order to provide better information to all instructors while supporting groups of instructors in learning from student activity data to demonstrate its value in course and curriculum design.
Please visit the Project Initiatives page for more information on major project work currently underway.
- Data-Driven Design: Quercus Analytics (D3:QA)
- Instructor-Facing Quercus Dashboard
- Program-Level Data Exploration
- Quercus Record Store
- Quercus Record Store
Learning Analytics Project Leadership at UofT
Under the leadership of the Vice-Provost, Innovations in Undergraduate Education, the Learning Analytics Initiative includes a Steering Committee and Project Team. The Steering Committee provides guidance setting the priorities for the advancement of University of Toronto learning analytics initiatives and input to the work of the Project Team. The Project Team is tasked with operationalizing our flagship learning analytics Initiatives.
| Learning Analytics Steering Committee | Learning Analytics Project Team |
| Susan McCahan (VPIUE) | Chair: Alison Gibbs (Director, CTSI) |
| Dwayne Benjamin (VPSEM) | Jeff Burrow (Project Officer, OVPIUE) |
| Alison Gibbs (Director, CTSI) | Marco Di Vittorio (Manager, ACT) |
| Kiren Handa (Director, IRDG) | Laurie Harrison (Director, DLI) |
| Bo Wandeschneider (CIO) | Annie Hua (Project Manager, ACT) |
| Sandy Welsh (VPS) | Avi Hyman (Director, ACT) |
| Alan da Silveria Fleck (Data Analyst, CTSI) | |
| Jeffrey Waldman (IRDG) |
Projects
Flagship initiatives withing the broader Learning Analytics program of activities are as follows:
Data-Driven Design: Quercus Analytics (D3:QA)
This initiative provides support to instructors to use student data to make course redesign decisions aimed at improving instruction and learner experience. A cohort of six instructors from various divisions have explored Quercus learner data through analysis of downloaded course activity and quiz reports with support from learning data analysts. Visit the D3:QA Project page for more information.
Instructor-Facing Quercus Dashboard
Phase 1 of this project has involved extensive community consultation in planning for a set of user-friendly dashboards, which can be accessed by any instructor to inform course design and pedagogical outcomes. As a result of the consultations, the first priority identified for exploration and proof of concept was it student activity and progression through course digital resources and the ability to track the type of activity throughout the term (e.g., number of clicks by day and by resource). Phase 2 is focused on development of integrated prototypes, using Quercus data to address this first priority area . Visit Quercus Instructor-Facing Dashboard Project page for more information
Program-Level Data Exploration
This project is engaging a departmental team in collaboratively developing a series of questions that explore how course-level data can inform program-level design. The departmental team is working with a data analyst to identify required data, produce analyses and visualizations of the data, review results, and propose changes to curriculum and program structure. In the current initial phase, this project will provide a proof of concept for availability of data, data governance and workflows to extract and analyze data using existing and/or manual processes. It will also inform development of future program level reporting processes and dashboard tools, to improve student learning outcomes within program areas. The main source of data for this pilot is student activity data from the Quercus learning management system. Quercus Record Store
In order to create infrastructure for the delivery of learning management system data to current and future learning analytics projects, work is underway to architect and implement the Quercus Record Store (QRS), which will allow the University of Toronto (UofT) to leverage Canvas Data Services (CDS) to ingest, store, process and analyze Quercus usage data securely and at scale. Data from the QRS will be integrated into the institutional data hub to allow for analysis alongside other administrative information sources. A key objective of the QRS is to facilitate data analysis through the creation of curated datasets that will be used for the Instructor-Facing Quercus Dashboards. Tooling will also be implemented to power ad hoc and prepared operational reports run against the full CDS dataset.