Shifts and Transformations in Canadian Postsecondary Teaching and Learning: Views from Teaching and Learning Centre Leaders
Bibliographic record
Abstract
Teaching and learning centres (TLCs) play a critical role in helping universities advance their strategic priorities related to teaching and learning. TLC leaders have a comprehensive lens that provides insights to help academic institutions move forward in addressing the shifts we are experiencing in postsecondary education. The purpose of this project was to conduct an environmental scan that broadly explores the shifts, transformations, and changes that TLCs are experiencing at Canada’s U15 institutions (large, leading research-intensive universities). We summarized these shifts, grounded key themes in scholarly research, and included recommendations to guide the work of TLCs and postsecondary institutions now and into the future. Teaching and learning centre leaders from U15 institutions (n=22) participated in semi-structured interviews over the fall and winter of 2024/25. Our themes surfaced from guiding questions that identified emerging and enduring shifts and transformations, the impact of these shifts, key priorities, and how to best strengthen postsecondary teaching and learning across institutions. Seven areas of focus for TLCs emerged, which are all grounded in and echoed by relevant academic literature: reflecting value; academic innovation and transformation; Indigenization, decolonization, and reconciliation; equity, diversity, inclusion, and accessibility; learning-focused; strategic priorities, planning, and resources; and, connecting, leadership, and well-being. These seven areas of focus informed our recommendations. TLCs play a critical role in addressing the challenges and changes affecting teaching and learning in post-secondary settings, now and into the future. There is a need for both academic transformation and relational transformation to address future priorities in higher education. TLCs should foreground strategic and relational academic leadership that commits to an ethos of compassion and care for those connected across the teaching and learning landscape. We hope this discussion paper will inspire dialogue amongst Canada’s postsecondary leaders, teaching and learning centres, and the broader academic community to enhance the work of TLCs and strengthen the quality of teaching and learning across institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.054 | 0.023 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".