Creating Communities of Inquiry in Research and Scholarship Via Online Videos
Bibliographic record
Abstract
Fostering a sense of community in postsecondary education is challenging. Accomplishing this from a distance via online education makes it even more difficult by inserting an additional layer of complexity. The COVID-19 pandemic necessitated a rapid shift to increased remote communication, collaboration, and research dissemination. Drawing on my experience as both an online instructional designer and a doctoral student who began my PhD amid a global pandemic, I use Garrison et al.’s community of inquiry (CoI) framework to explore how online videos and webinars (both synchronous and asynchronous) can be used to foster a sense of community, inspire learning, and support research sharing in virtual environments. RÉSUMÉEncourager un sentiment de communauté dans l’enseignement postsecondaire est un défi. Le faire à distance par l’enseignement en ligne est encore plus difficile car on ajoute un niveau supplémentaire de complexité. Or, la pandémie de COVID-19 a rapidement augmenté le besoin de communiquer, de collaborer et de diffuser la recherche à distance. Dans cet article, en m’appuyant sur mon expérience de conceptrice d’enseignement en ligne et d’étudiante de 3e cycle ayant commencé son doctorat au milieu de la pandémie, j’utiliserai le modèle de la communauté d’enquête de Garrison et al. pour explorer comment on peut utiliser les vidéos en ligne et les webinaires (synchrones et asynchrones) pour favoriser un sentiment de communauté, inspirer l’apprentissage, et soutenir le partage de la recherche dans des environnements virtuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".