Transitioning University Courses Online in Response to COVID-19
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
As the world reeled from the realization that a pandemic of a magnitude not seen in a century was upon us, and that physical distancing to reduce the speed of transmission was going to necessitate suspension of regular classes, university faculty members scrambled to convert their planned lectures from in-person to online formats. This article describes one faculty member’s experiences using a flipped classroom approach in a virtual teaching environment. The arrival of COVID-19 fractured the school year and put some students’ graduation in jeopardy. From a hasty search of literature on the process of teaching and evaluating in an online environment, to a selection of hardware and software to provide students with an optimal learning environment while ensuring the security and validity of online evaluation, this article will highlight some of the successes and pitfalls of a rapid transition to online instruction and evaluation. Although there is a body of literature on the process and efficacy of online teaching, the constantly evolving nature of technology not only continues to produce new online instruction tools, but also tools that can be used by students to circumvent most cheating prevention measures put in place.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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