Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,002 | 0,017 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».