An exploration of instructors' and students' perspectives on remote delivery of courses during the COVID‐19 pandemic
Notice bibliographique
Résumé
Abstract The world‐wide pivot to remote learning due to the exogenous shocks of COVID‐19 across educational institutions has presented unique challenges and opportunities. This study documents the lived experiences of instructors and students and recommends emerging pathways for teaching and learning strategies post‐pandemic. Seventy‐one instructors and 122 students completed online surveys containing closed and open‐ended questions. Quantitative and qualitative analyses were conducted, including frequencies, chi‐square tests, Welch Two‐Samples t ‐tests, and thematic analyses. The results demonstrated that with effective online tools, remote learning could replicate key components of content delivery, activities, assessments, and virtual proctored exams. However, instructors and students did not want in‐person learning to disappear and recommended flexibility by combining learning opportunities in in‐person, online, and asynchronous course deliveries according to personal preferences. The paper concludes with future directions and how the findings influenced our planning for Fall 2021 delivery. The video abstract for this article is available at https://www.youtube.com/watch?v=F48KBg_d8AE . Practitioner notes What is already known about this topic Emergency Remote Teaching (ERT) allowed institutions across the world to continue teaching and learning at all levels of education during the COVID‐19 pandemic. However, this form of delivery, created under conditions of uncertainty, was developed out of an urgency to keep education going rather than maintaining it at the same level. What this paper adds This study comes after ERT, and is situated between ERT and the return to campus, with some social distancing restrictions still active, in a delivery method widely viewed as “remote delivery”. This is a case study of an entire Canadian higher education institution that implemented remote learning for over one full academic year, documenting and examining instructors' and students' experiences and challenges of the remote learning course delivery format. Quantitative and qualitative data were collected to provide a holistic overview of instructors' and students' experiences of delivery method and assessments including the use of face‐tracking proctoring software. Implications for practice and/or policy Compared to ERT, remote delivery was a thoughtful and deliberate way to transform in‐person courses into virtual learning experiences. Instructors and students were able to successfully replicate many features of in‐person learning and assessments experiences in remote delivery of courses by using effective online tools to teach and learn. As a result, instructors and students called for the use of elements of remote delivery to create more flexible learning opportunities by combining in‐person, live streaming, and asynchronous learning options.
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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 | 0,009 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».