Large Discussion Groups’ Impact on Engagement and Community
Bibliographic record
Abstract
To address the solitary nature of online learning, asynchronous micro discussion tools can be implemented to enrich students’ learning experiences by encouraging interaction among students, nurturing social presence, and facilitating community development. Students’ experiences using an asynchronous micro discussion tool in online learning were investigated, with a focus on engagement in learning with peers, sense of community, and technology’s effectiveness. Survey data was analyzed from two sections of an online introductory course from 458 postsecondary university students. As part of the course evaluation, students were asked to participate in three, intentionally designed discussions using an asynchronous micro discussion tool. In each instance, the discussion topic was tightly linked to course content, promoting collaborative, reflective learning. Results indicated that while students felt that learning through asynchronous micro discussions was effective, they did not feel a strong sense of community using this method. However, students who experienced increased engagement also reported having a better understanding of the course material, fewer technological issues, and felt a stronger peer-to-peer connection. Importantly, collaborative learning that increases engagement does not appear to be negatively influenced by large group size in higher education. Given the prevalence of the online learning environment this is valuable course design information.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".