Putting community first: supporting (a)synchronous interaction and belonging in online learning
Bibliographic record
Abstract
The challengeStudent Academic Success Services (SASS) is a writing centre and learning strategies unit that supports the 24,000 students at Queen's University in Kingston, Canada.We do not directly support faculty's pedagogical development and instructional design.Instead, we provide students with academic skills and writing support through workshops, appointments, and drop-in sessions and groups.SASS's mission is as much about students' lives as their learning experiences.We believe that supporting students' sense of belonging and community are central to academic success (for example, Bliuc, et al., 2011;Strayhorn, 2012;Reynolds et al., 2017;Suhlmann et al., 2018), and help them persist in the face of challenges (Hoffman et al., 2002;Strayhorn, 2012).Developing a sense of belonging leads to feelings of community, and community boosts success.The abrupt transition to online learning in March 2020 threatened to hit our communitybuilding mission hard.Gone were the shared spaces and sites of interaction.Gone were the opportunities for structured writing groups, workshops and drop-in programmes.Gone were the chance meetings, the reassuring nods of a classmate, and the safety in numbers of the typical learning community.Worse still, gone were the opportunities to identify students who struggled to engage in the community at a large institution.Heggie and Garner Putting community first: supporting (a)synchronous interaction and belonging in online learning
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".