Digital Tools Faculty Expected Students to Use During the COVID-19 Pandemic in 2021: Problems and Solutions for Future Hybrid and Blended Courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covid-19 resulted in a pivot to remote teaching and learning in most North American colleges and universities. All of a sudden faculty expected students to use a variety of digital technologies. Here we report on the technologies post-secondary students had to use and on the problems experienced by students with and without disabilities (e.g., mobility and visual impairments, attention deficit hyperactivity disorder, mental health related disabilities). In a sample of 24 post-secondary students, we found a series of problems related to: software and platform issues; connectivity; how professors managed their courses; classmates’ computer behaviors; and equipment issues. We also learned about several beneficial practices and ways to avoid problems that can be retained for future hybrid and blended courses. By giving a voice to post-secondary students our research can inform policies and practices to create a more resilient and inclusive society.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it