Benefits and Challenges of Zoom Tutoring during the Covid-19 Pandemic
Bibliographic record
Abstract
This study aimed to evaluate the benefits and challenges of remote/online tutoring using Zoom software/platform at a Canadian university’s Writing Centre during the Covid-19 pandemic in 2020/21. In addition to gathering data on the benefits and challenges of online tutoring, this study also provided work and research experience for a Work-Study student in the host department. The study adopted a mixed methods quantitative and qualitative approach where the employed tutors and tutees that came to the Writing Centre that term were invited to complete a survey asking them about their experience with remote/online tutoring on Zoom. The results indicated that tutors expressed a high rate of satisfaction and preference for Zoom tutoring. In contrast, tutees, although appreciative of the convenience of Zoom tutoring, demonstrated preference for an in-person face-to-face method of tutoring. Some of the benefits of Zoom tutoring for both tutors and tutees were flexibility, working from a comfortable setting like home, not having to secure childcare, and zero commute time. Some of the challenges of Zoom tutoring included technical glitches, isolation from peers and colleagues, lack of motivation, and time zone difference challenges. Besides providing valuable information for the future delivery of Writing Centre services, this study also gave the Work-Study student indispensable experience in conducting primary research. This study received ethics approval from the University Human Ethics Research Board.
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.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".