Open, Flexible, and Serving Others: Meeting Needs during a Pandemic and beyond
Bibliographic record
Abstract
Abstract In the early days of the COVID-19 pandemic when in-person courses were switching to emergency remote learning formats, even students online needed flexibility. This case study describes how a graduate-level online class on open learning and open educational resources (OER) was redesigned to both allow students to apply their course-related knowledge and skills in the service of others and accommodate students whose other life responsibilities had changed. Findings show that these online students experienced great stress during Spring 2020, and many had increased job duties related to the shift to remote learning. These students appreciated the flexible redesign and used it as an opportunity to help integrate OER in their own remote teaching and assist others to do the same. They provided their colleagues and the field at large with educational resources about finding, using, creating, and sharing OER, all while meeting course objectives. Even students who were not employed as educators or instructional designers embraced the opportunity to be helpers and deploy their new knowledge and skills. Student learning outcomes were assessed using reflective portfolios, and course objectives were met whether students followed the original course plan or took advantage of the course redesign.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".