Bibliographic record
Abstract
This study examines how university students and professors engage and are impacted in remote learning and teaching during COVID-19. The goal of this paper is to explore the evolution and challenges faced by students and professors alike of early digital/remote education from the early 2000s and the way up to contemporary years, 2020 and 2021 due to COVID-19. This paper also presents findings from interviews with university students and professors of various backgrounds and explores solutions that can potentially alleviate challenges faced by them. The documentary supports this study by archiving events and information discussed by students and professors as well as providing an insight into the daily lives of 6 student participants and 1 professor participant during COVID-19, ages range from 22 to 40 years. Participants were tasked with filming themselves and given weekly themed topics such as “What is normal?” to discuss, participation length ranged from 2-6 weeks. Incorporating daily lives of participants and their thoughts creates a more intimate and holistic overview of the impacts of COVID-19 not just on education but also other aspects that are not often discussed. A director’s cut will also be released in the 2021 September or October period which showcases more interviewees and participants’ recorded footage to give current and future educators a better understanding on the impact of remote learning and teaching during COVID-19. The results show that COVID-19 has not only changed how students learn and how professors teach, but also how both parties were affected in other ways. There is a constant theme of stress stemming from school, family, quarantine, and isolation. There are also many other factors that are not often discussed such as barriers to education due to lack of infrastructure or restriction of international students traveling back to Ontario, Canada for education, etc. The desire for in-person classes by students and professors is persistent due to various reasons including but not limited to decrease in motivation and lack of engagement via Zoom when students turn off their cameras. The idea of a hybrid education system is extremely welcomed by students and lesser so amongst professors though they are intrigued; unfortunately there is no consenus on how hybrid classes should be run as opinions differer greatly. This study concludes that the first step to improving the quality of remote learning and teaching during COVID-19 is to establish better and more frequent communication between students and professors. Finally, a new era of digital/remote education will likely happen between 2021 Fall term or around 2022 Winter term depending on COVID-19 situations and other factors.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".