How a learning skills course addressed transition, diversity and inclusion, and a sense of belonging for mature students seeking entrance to university: reflections of a Canadian learning specialist
Bibliographic record
Abstract
Universities across Canada offer bridging programmes for mature students who would not otherwise have access to post-secondary education. The College of Arts at the University of Guelph developed their Academic Transition Program to support these students, with the cornerstone of the programme being a learning skills course, launched in Autumn 2022, that students must complete in order to be accepted into an undergraduate programme. In the Canadian context, it is unusual for a learning specialist to act as course developer for the creation of an undergraduate credit course. This presentation shares a reflection on the theories that underpinned the course creation, most notably Kolb’s (1984) theory on experiential learning, Baxter-Magolda’s (1999) theory of self authorship, and the learning gained after wearing many hats – learning specialist, course developer, and sessional instructor. The presentation explored: The tripartite arrangement developed to create the course. Ways in which the course addressed students’ transition to university. Considerations around diversity and inclusion. How the coursework supported a sense of belonging. Feedback from the students’ experience of the course. What was learned when the course was made available to traditional undergraduate students from first through fourth year. How this course intersects with the Canadian model of learning support. Sharing examples of course content, including weekly reflection questions. Lessons learned and plans for the future of the course, including alternative formats. Making learning strategies explicit can support mature students’ level of success in higher education (Erb & Drysdale, 2017). This course combined theoretical and practical learning skill applications and opportunities to develop a sense of belonging for a diverse cohort of mature students.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.076 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".