Notice bibliographique
Résumé
This qualitative multi-method study investigated faculty member perspectives on e-learning policy, and its influence on their use of e-learning. The research was conducted at one medium sized comprehensive university in Ontario, Canada. Data were collected from interviews with 12 full-time faculty members, eight of whom had taught at least one online undergraduate university course. Data were also collected from institutional and government documents. Respondents noted e-learning increased flexibility and/or convenience with respect to both their engagement with students, and student engagement with course material. E-learning was identified positively for its ability to save time by some respondents, and negatively as being time intensive by others. Increased student and government demand for on-line courses, as well as the opportunity to use technology for instructional purposes, increased respondents’ use of e-learning. Additionally, the university’s pedagogical centre, which provided direct support to respondents, was considered key in supporting their transition to e-learning. Respondents were generally unable to identify specific university policy related to e-learning, and some noted the lack of specific policy had hampered e-learning course development in their departments. The documents reviewed tended to view e-learning in favourable terms, highlighting it as a response to changing political, economic, and societal conditions, and promoting it for its ability to reduce costs to the university, increase student enrolment, and provide more equitable access to university programs, particularly for under-represented groups such as new Canadians, Indigenous peoples, and first-generations students. Whereas government documents tended to focus on mandates (e.g. the intent to change the university system based on each university’s strengths), institutional documents focused on teaching, learning, and e-learning, both in response to government mandates, and in alignment with the University’s strategic direction. Collectively, the documents shared the respondents’ perceptions regarding flexibility, time, and demand. However, while government documents focused on issues of cost, changing conditions, enrolment and equitable access, institutional documents explained e-learning, the differences with face-to-face teaching and learning, and how best to integrate e-learning into practice.
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 | 0,014 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,032 | 0,013 |
| Communication savante | 0,011 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».