Equalizing student and teacher: Using COVID-19 to (re)imagine curriculum
Bibliographic record
Abstract
COVID-19 created an opportunity to (re)envision students as partners in curriculum development and the curriculum process. Understanding the design and delivery of courses as a flattened hierarchy, particularly with graduate students as partners, is the focus of this study. This article reports findings from research undertaken collaboratively with students as partners in developing a new approach for conducting a capstone course and project. This research was enacted at a research-intensive university in the United States in 2020 and 2021. We describe the need for the shift in stance to students as partners in our institution as well as what the findings indicate as imperatives for teachers in both K–12 settings and institutions of higher education. The findings indicate how teachers’ mental health and experiences of stress were affected by specific attributes of the pandemic and pandemic teaching (which aligns with the majority of COVID-19 research in education), as well as how some learned to cope with these demands. Findings also indicate the need for flexibility in all learning environments.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".