Developing Learning Communities Online
Bibliographic record
Abstract
While online course delivery in higher education has been increasing for several decades, students can face unique challenges in the digital environment. At a small university in Western Canada, online and blended learning have been a major focus for course delivery since 1995. Considering the risk that students could experience a lack of meaningful connection with their fellow students, the university launched a not-for-credit online learning module in 2006 that was designed to provide new-to-program students with resources and activities to encourage learning community development. Since the first module was launched, several programs at the university have adapted the original module to suit their specific needs. In this paper, we explore the experiences of graduate students in three programs over an eight-year period. Students completed surveys focused on the role of three module activities in helping them develop a supportive online learning community. The findings were organized under three areas that revealed elements of the module that worked well, areas for improvement, and suggestions for module additions. The recommendations call for making modules that are not-for-credit, mandatory, support both synchronous and asynchronous collaboration, use only one web-based entry point, consider time zones, and support students’ ability to balance their education with their out-of-school commitments. For those who may wish to include similar activities for their students, we have included a link in the paper to the Open Educational Resource that was developed in support of our research.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".