Addressing gaps in equity: a review of best practices in multi-campus learning to enhance teaching, social and cognitive presence
Bibliographic record
Abstract
Evaluating student learning experience in multi-campus classes is an excellent way to improve course design and delivery by educators. The community of inquiry (CoI) framework by Garrison, et al, 2000 was converted into a survey tool by Arbaugh, et al, 2008. The Community of Inquiry Online Survey Tool (COST) is an online implementation that was developed by Sielmann, et al, 2022. COST is intended to be a fast and convenient way of identifying inequity between cohorts. The research question motivating this work is: “In scenarios where a divergence in perceived student experience exists between cohorts in multi-campus courses, what specific pedagogical best practices aligned with deficiencies in student perceived CoI presence can aid in achieving greater equity between cohorts in student experience?” A systematic literature search was conducted. Sources of gaps in equity across cohorts were identified. Pedagogical best practice statements that can be incorporated into COST were synthesized.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".