‘What if my Wi-Fi crashes during an exam?’ First-year engineering student perceptions of online learning during the COVID-19 pandemic
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 COVID-19 pandemic necessitated a rapid transition to remote education in post-secondary institutions. To understand the first-year engineering undergraduate student perceptions of this transition to online learning, surveys were administered in two design-focused first-year engineering courses with a total of 201 enrolled students. A thematic qualitative analysis of open-ended survey questions resulted in 7 themes: Health & Safety, Growth Mindset, Student Agency, Course Design, Coping/Management, Execution, and Technology. Students expressed positive and negative perceptions of remote education and included opinions related to current and future learning, and future careers. Most student perceptions were grounded in fear of the unknown, and student mental health emerged as a predominant undercurrent in the data. The identified themes and underlying student perceptions suggest that instructors teaching online should aim to (1) support communication, collaboration, and student engagement, (2) promote meaningful learning and growth mindsets, and (3) foster strong learning partnerships and class experiences.
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 | 0.001 | 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 | 0.001 |
| 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