Student Experience of Emergency Remote Teaching During COVID-19 Early in the 2020/21 Academic Year
Bibliographic record
Abstract
In response to the contagious disease coronavirus disease 2019 (COVID-19), a number of health and safety measures were enacted across Canada in March 2020. These measures included the physical closure of postsecondary institutions, including the authors’ institution Carleton University. The physical closure resulted in an abrupt transition from normal in-person teaching to emergency remote teaching (the term emergency remote teaching is used to distinguish it from online teaching, which is not subject to the challenges and constraints associated with an emergency situation). Emergency remote teaching continued at Carleton University for the entire 2020/21 academic year. There were increased resources, training opportunities, supports, and time to prepare for teaching, as compared to the sudden change in the Winter 2020 term. Simultaneously, there were still many ongoing challenges and constraints in the pursuit of optimal remote teaching and learning. Midway through the Fall 2020 term, a questionnaire on the student experience with emergency remote teaching was developed and delivered to undergraduate students in the Department of Systems and Computer Engineering at Carleton University. This paper presents the findings of this questionnaire from the 159 respondents. Results suggest that, on average, academic and intellectual student engagement was slightly worse for emergency remote teaching versus normal in-person teaching. Emergency remote teaching posed some difficulties and challenges, but also provided some advantages that were preferred (e.g., less travel time, ability to rewatch asynchronous lectures). There was a notable worsening of social student engagement, which was associated with increased feelings of isolation and decreased mental health and well-being. There was also a number of students who faced technical barriers with respect to remote teaching, with only 22% indicating that they experienced few or inconsequential technical barriers. This research adds to the discourse on emergency remote teaching, including the lens of engineering education. The paper can help inform future transitions to emergency remote teaching, with some insights potentially useful for online teaching, which is anticipated to continue to increase in its prevalence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".