Transdisciplinary, Community-Engaged Pedagogy for Undergraduate and Graduate Student Engagement in Challenging Times
Bibliographic record
Abstract
When the COVID-19 pandemic required all higher education learning to move to remote or online formats, students were challenged to maintain a sense of community and to advance in their education. By focusing on the immediate, human needs of students, IdeasCongress - a community-engaged experiential learning course with a curricular emphasis on transferable skills - flourished in the remote synchronous format. The only significant change was to shift the topic of the course to #RecoverTogether to guide our students in imagining a path through the pandemic while supporting local charities by developing plans for mitigating the impact that the pandemic was having on their service model. This paper outlines a case study of the course and reflections upon the experience of teaching during the pandemic restrictions, supported by student feedback from the September-December (Fall) 2020 semester. Based on this evidence, the approach appeared to be effective for student retention and engagement, and increased student feelings of connectedness to both the campus and the local community. The paper highlights key lessons learned while teaching and learning during challenging times and describe the teaching approaches used to support 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".