Collaborative Learning in a Large Enrollment Online Course: Application of the Community of Inquiry Framework
Bibliographic record
Abstract
This study used the Community of Inquiry (CoI) framework to provide a collaborative experience for students from marginalised communities in a large enrollment module in South Africa. The CoI framework consists of three elements such as the social, cognitive, and teaching presences. This study focused on the teaching presence element along with the seven principles in the CoI framework which involves design and organisation, facilitation, and direct instruction. The study context was one large English language application course which enrols over 1200 students who speak English as an additional language at an open distance e-learning university in South Africa. The research was a self-study approach and the course was designed over a few months to accommodate students and create collaboration amongst them. The findings revealed that the course structure engaged students, provided personal introductions and goal-setting opportunities, incorporated motivational content which increased student engagement and collaboration. Communication in the course was facilitated through familiar social media channels to provide alternative content modes which further supported student learning. Direct instruction which involved explicit assignment support and feedback was crucial to ensure that students achieved the learning outcomes. The findings of this study highlight the significance of the CoI framework and its seven principles to create a collaborative and engaging learning context in large enrollment courses.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".