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
Read more about our learning analytics guiding principles.
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 in 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, ARC) |
| Kiren Handa (Executive Director, IRDG) | Laurie Harrison (Director, DLI) |
| Bo Wandeschneider (CIO) | Annie Hua (Project Manager, ARC) |
| Sandy Welsh (VPS) | Avi Hyman (Director, ARC) |
| Alan da Silveira Fleck (Data Analyst, CTSI) | |
| Jeffrey Waldman (Manager, Institutional Data Governance, IRDG) |
Learning Analytics Initiative Activities
The Learning Analytics Initiative at the University of Toronto is focused on supporting instructors in improving course and curriculum design. Initial projects include building technology infrastructure in order to provide better information to all instructors and supporting groups of instructors in learning from student activity data to demonstrate its value in course and curriculum design.
Flagship initiatives within the broader Learning Analytics program of activities are as follows:
Quercus Data Insights
Quercus Data Insights (QDI) is a dashboard developed in Microsoft Power BI, and connected to the data from the Quercus Record Store. With the QDI dashboard, instructors can view aggregated student activity on Quercus, allowing them to gain insights into students’ interactions with course resources.
In the current project phase, a dashboard with data from September 2022 to August 2024 is available for instructors who teach a course using Quercus. The dashboard data is currently not being updated daily. The next project phase will be the implementation of daily refreshes of the activity data. For more information, visit the QDI project page.
Quercus Record Store
The Quercus Record Store (QRS) is the infrastructure for the delivery of student activity data to current and future learning analytics projects. The creation of the QRS allows the University of Toronto (UofT) to leverage Canvas Data Services (CDS) to ingest, store, process and analyze Quercus usage data securely and at scale. A key objective of the QRS is to facilitate data visualization and analysis through the creation of curated datasets that are used for Quercus Data Insights. The QRS will also include tooling to power ad hoc and prepared operational reports from the full CDS dataset.
Data-Driven Design: Quercus Analytics (D3:QA)
This initiative supported instructors in using student data to make course redesign decisions aimed at improving instruction and learner experience. A cohort of six instructors from various divisions explored Quercus student activity data through the analysis of downloaded course activity and quiz reports, supported by learning data analysts. Visit the D3:QA Project page for more information.
Program-Level Data Exploration
This project engaged a departmental team in collaboratively developing a series of questions to explore how course-level data could be used to inform program-level design. The departmental team worked with a data analyst to identify required data, produce analyses and visualizations of the data, review results, and consider possible changes to curriculum and program structure. The project provided a proof of concept for the availability of data, data governance processes, and workflows to extract and analyze data using existing automated and/or manual processes. The main source of data for this pilot was student activity data from an early iteration of the Quercus Record Store.