Bibliographic record
Abstract
This article examines how a professor co-creates educational YouTube videos alongside students to form a community of inquiry (CoI). The CoI considers the cognitive, social, and teaching presences in the educational experiences of technologies. In this case, students and a professor co-create YouTube videos as a basis for interactivity and collaboration within the CoI in order to resolve teaching and learning challenges. The making, understanding, and implementation of videos is a blended learning approach that fosters competency development and pedagogical praxis. The multimodal nature of YouTube encourages the students to become active producers of their learning through their user-generated videos. This participatory culture is necessary for online, in-person, and blended teaching and learning realities. Practical implications of the co-making process and the video workflow are provided. RÉSUMÉCet article examine comment un professeur cocrée des vidéos YouTube éducatives avec des étudiants dans le contexte d’une communauté de recherche. Celle-ci prend en compte les présences cognitives, sociales et pédagogiques dans l’expérience éducative des technologies. Dans le cas qui fait l’objet de cet article, un professeur et des étudiants forment une communauté de recherche afin de cocréer des vidéos pour YouTube. Pour ce groupe, il s’agit d’une occasion d’interactivité et de collaboration en vue de résoudre des défis d’enseignement et d’apprentissage. Ainsi, la création, la compréhension et le téléchargement de vidéos constituent une instance d’apprentissage hybride qui favorise le développement de compétences spécifiques et l’application pédagogique de celles-ci. La nature multimodale de YouTube encourage les étudiants à participer activement à leur propre apprentissage au moyen des vidéos qu’ils cocréent. Cette culture participative est utile aux réalités de l’enseignement et de l’apprentissage, qu’elle se passe en ligne, en personne ou sous forme hybride. L’article explore aussi les implications pratiques du processus de cocréation et de la séquence de tâches requises pour créer une vidéo.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".