Teaching note—teaching and learning during the COVID-19 lockdown at the university of Windsor: Faculty, graduate teaching assistant and student experience
Bibliographic record
Abstract
In response to the global upheaval caused by the COVID-19 pandemic, educational institutions, including the University of Windsor, transitioned swiftly to virtual learning, necessitating innovative approaches to ensure academic progress amidst the cancellation of in-person classes and exams. This transition was particularly significant for the University of Windsor, situated in Southwestern Ontario, where the pandemic's impact was felt deeply, with implications for both the university community and the broader region. Despite initial challenges, the subsequent summer semester saw smoother operations, attributed to collective learning experiences among faculty, graduate assistants, and students, particularly in the School of Social Work. This paper examines the delivery of a Master of Social Work course, Challenges in Human Behavior, during the pandemic, showcasing the use of virtual platforms and innovative assessment strategies. Insights from faculty, graduate assistants, and students reveal varying experiences and challenges, highlighting the importance of proactive communication, support mechanisms, and student-led initiatives in enhancing the online teaching and learning experience. As the educational landscape continues to evolve amidst uncertainty, these findings offer valuable recommendations for preparing educators, fostering instructor-student communication, and empowering students as active participants in their educational journey, ultimately shaping the future of online social work education and beyond. This study underscores the resilience and adaptability of educational institutions in navigating unprecedented challenges, while also recognizing the ongoing need for collaboration and innovation in shaping the future of higher education in a rapidly changing world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".