The Experience of Tests during the COVID-19 Pandemic-Induced Emergency Remote Teaching
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
The dire circumstances presented by the COVID-19 pandemic have had a severely debilitating global impact on education, and led to an urgent transition from the onsite environment (OSE) to the online environment (OLE) for teaching and learning. In that regard, this paper describes the experiences of us and students of our involvement in oral and written tests in multiple software engineering-related courses during 2020 and 2021. The challenges encountered along with the interventions are discussed, and educational lessons based on the reactions and responses of the students are given. The results of a preliminary survey of the students of their learning experience in the OLE are presented and, related to it, the comments from the students highlighting their preferences of the OSE or the OLE are included. The test procedures, processes, and/or practices herein are, in principle, generalizable and potentially applicable to other courses in computer science or software engineering, during emergency remote teaching or even otherwise.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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