Institutional collaborations to support undergraduate research as experiential learning
Bibliographic record
Abstract
Changing a campus culture to have experiential learning become essential to the student experience requires extensive buy-in and support from the campus community (Stowe & O'Connell, 2022). The Office of Experiential Learning at the University of Calgary supports experiential learning institutionally, with a bold target of all students having at least two high-quality experiential learning opportunities during their academic studies (Kaipainen et al., 2020). At UCalgary, a large, research-intensive institution, undergraduate research is a signature form of experiential learning where students engage in hands-on research in collaboration with researchers. There are numerous benefits to students participating in research (for example, see Lopatto et al., 2020); however, a lack of incentives and training can limit faculty members' willingness to engage in pedagogical change (Brownell and Tanner 2017). In this panel discussion, we will describe the landscape and context of undergraduate research at UCalgary, programming that has supported more than 3500 students engaging in research initiatives over the last three years, and institutional-level collaborations that have facilitated a culture shift, increasing undergraduate research in curricular and co-curricular experiences. As a vital part of our work, we will describe extensive research to understand barriers to accessing experiential learning opportunities for deserving equity groups and ameliorative actions we have taken as a campus to increase accessibility (Stowe et al., 2022). Finally, we will discuss the challenges and successes faced in expanding undergraduate research and experiential learning opportunities in our context institutionally and explore with participants how our experiences may translate to other institutional contexts.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".