Supporting Equity in Online Learning during COVID-19
Bibliographic record
Abstract
As universities and colleges around the world moved online in response to the COVID-19 pandemic, questions were raised about the impacts of this transition on institutional commitments towards and capacity to provide equitable learning environments: access to all students, inclusive experiences within courses and/or programs, and achieve equitable outcomes across all intersections of diversity—including, but not limited to, race, gender, sexual orientation, disability, socio-economic background, levels of parental education, access to technological resources, and geographic location. In this chapter, we report on our systematic review of institutional policies and communications in response to the pandemic at four different Canadian universities to explore how they attended to issues of equity and student success in an online/remote environment. As part of a larger data set, the research team analysed existing institutional policies related to online learning, the public institutional communication (particularly to students) about the transition online, and the explicit commitments made to support the creation of equitable learning environments for students. This examination of how equity has been framed and addressed during a time of global crisis will have implications for the future development of equitable learning environments in higher education.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".