Notice bibliographique
Résumé
In the last three decades, information and communications technology (ICT) have evolved very rapidly.Among other things, it resulted in new opportunities in course delivery modes: Online and blended courses (Siemens et al., 2015).A course delivery mode refers to the decisions about the way teaching, learning, and assessment activities are conveyed to students.Thanks to ICT, these delivery modes offer advantages both to students and to postsecondary institutions.On the one hand, depending on their design, blended and online courses have the potential to meet the demands of students who desire flexible course schedules, such as adult students, because of family and/or work responsibilities (Lakhal, 2019).They also give students better access to higher education, especially in large countries such as Canada where distances can be quite significant between students' homes and their colleges or universities (Lakhal et al., 2020), and they allow both synchronous and asynchronous contacts between students and with the instructor, even if students would not need to travel as much to attend face-to-face sessions (Cidral et al., 2018).On the other hand, online and blended courses provide colleges and universities with some financial benefits (Gosmire et al., 2013), as they can free up space on campuses, since students and instructors no longer have to meet face-to-face in a classroom for each class session.Moreover, as some meta-analyses (Bernard et al., 2004(Bernard et al., , 2014;;Means et al., 2013) have revealed, blended and online courses appear to be at least as effective for student learning as face-to-face courses delivered on campus.A number of models have been proposed to categorize the modes by which courses can be delivered.Most of them oppose the proportion of courses taught face-to-face to those mediated by online technologies (Graham, 2013).As reported by Graham (2013), other authors have used different criteria as the basis of their model.For example, from formal to informal learning, or as a continuum from face-to-face instructor-led learning to real-time workplace learning supported by online technologies.These models, however, are better suited for corporate training.In higher education, HySup, an often cited European study, classified course delivery modes based on their educational, organizational, and material characteristics, resulting in six types of courses (Burton et al., 2011). Margulieux et al. (2016) classified eight course delivery modes using two criteria for their matrix: The proportion of instruction delivered face-to-face or by online technologies and the instructional approach used in the course, instructor-focused or student-focused.Many colleges and universities also developed their own institutional model of course delivery modes.For example, Mount Saint Vincent University proposes the following categories of courses 1 : Face-toface, blended, multi-access, synchronous online, asynchronous online, and multimode online.Université Laval has recently renewed its categories of courses 2 as follows: Faceto-face, blended classroom, blended distance, distance asynchronous, distance synchronous, and blended synchronous courses.Continuums have been suggested to classify different course delivery modes.The first one, proposed by Allen and Seaman (2007) and lately by Allen et al. (2016), placed 1 https://www.msvu.ca/academics/teaching-and-learning-centre-and-online-learning/take-onlinecourses/course-delivery-modes 2 https://www.ulaval.ca/les-etudes/formules-denseignement
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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,007 |
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 ».