Connections/Communities Impact on Online Learning
Bibliographic record
Abstract
Abstract Building a sense of community within online learning environments has taken on greater significance during the COVID-19 pandemic, where online learning has become essential given the suspension of in-person classes around the world. Theoretical concepts such as the Community of Inquiry theoretical framework (Garrison et al., 2000) and the Fully Online Learning Community Model (VanOostveen et al, 2016) offer a conceptual basis for understanding the importance of online communities. A method of measuring virtual communities is necessary to track both their development and identify curriculum and instructional practices that foster and maintain their success. Rovai’s Classroom Community Scale (2002) and other measurement tools were found to be critical for measuring student connectedness and learning, and how virtual communities can meet the educational needs of students. Furthermore, analyzing the implications of technology on user perception and sustainability of virtual communities is crucial. Widespread and equitable access to emerging technologies have enhanced multimodal forms of collaboration and interaction. Overall, online communities may prove to be beneficial to online learning, by eliminating the sense of isolation often felt in traditional distance learning classrooms and decreasing the attrition rate of online students as a result. Keywords: community, online learning, technology, measurement, connections
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".