You can’t throw snowballs over Zoom: The challenges of service-learning reflection via online platforms
Bibliographic record
Abstract
COVID-19 has pervaded all aspects of higher education. Instructors are scrambling to ensure students meet predetermined learning outcomes through online communication and teaching. Students are trying to learn, collaborate, and communicate in new ways with fellow classmates and instructors. As `traditional´ service-learning activities shift to accommodate physical distancing measures and remote learning, and students wrestle with the seismic shifts in their socio-political, economic, and cultural lives, critical reflection is now more important than ever. In this article, we draw on their collective experiences to discuss the importance of establishing an open, honest, and trustworthy environment for students to thoughtfully and productively engage in domestic curricular service-learning endeavours. Specifically, we examine the challenges of facilitating service-learning reflection activities for a fourth-year undergraduate media studies course at Western University (Western), a large, research-intensive publicly funded institution in Canada. The article concludes by offering some key recommendations for how instructors can effectively engage students in critical reflection via online platforms.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".