Science and Engineering Education as an Anchor in the Midst of a Changing World: The Case of Covid-19
Bibliographic record
Abstract
The discourse on science and engineering education focuses on ways of preparing students, as future employees, and global citizens. While this discourse deals with the purposes and characteristics of engineering education, it tends to neglect the students’ perspectives. The purpose of this study was to provide insights into the perspectives of undergraduate science and engineering students with respect to six factors, during the Covid-19 pandemic: end-of-semester exams, financial situation, social life, extension of study duration, the future of the labor market, and how the world will look. A comprehensive questionnaire was distributed to all undergraduate students in a research science and engineering university in two consecutive academic years. Descriptive statistics and content analysis were applied. Our findings show that science and engineering students were mostly concerned about their end-of-semester exams. Their social life was the only factor that changed between the two periods in terms of the percentage of students who were concerned with it. As for the other factors, the percentage of students who were concerned about them remained comparatively the same in both academic years. The findings highlight the confidence students had during the pandemic, and demonstrate the resilience of science and engineering, especially in times of volatility.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".