A Review of the Teaching Modalities Chosen by Faculty During the Global Pandemic
Bibliographic record
Abstract
Universities, worldwide, are managing their course offerings during the coronavirus pandemic in different ways and numerous factors are considered when selecting an appropriate teaching modality.In this paper, a research question is posed as follows: how do faculty members prefer to teach during the pandemic and what are the implications?Data is provided from the engineering and computer science faculty members at The University of Texas at Dallas, where faculty are individually offered a choice among five different teaching modalities.The results are used to quantify our faculty's selection and explain reasons for selecting a particular teaching modality.The required preparation, and the support offered by the university to the faculty during the pandemic are also addressed.Half the faculty, who taught virtual classes, consider the student's performance on assessments comparable to the performance of students during previous semesters when the class was given in-person.A quarter considers the student's performance better and a quarter considers it worse.Beyond the pandemic, the majority of the engineering and computer science faculty prefer flexibility between the classroom and remote teaching.Otherwise, they prefer teaching in the office over working from home.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".