Flourishing in Online Learning: Why Being Lost Together Beats Being Lost Alone
Bibliographic record
Abstract
In the decline of student well-being, more knowledge is needed on how synchronous and asynchronous learning models facilitate flourishing. To examine whether and how online learning modalities can enhance flourishing, this research examines a randomized controlled trial of two online versions of the same organizational wellness intervention (synchronous learning with video conferencing versus asynchronous self-guided learning) over ten-weeks on a large sample of Canadian business school students. Data was collected over two terms during COVID-19 and measures were assessed at baseline, program completion, and 4-weeks post-program. Applying theory from community of inquiry frameworks, we examine the role of a climate of authenticity as a program mechanism that explains increases in flourishing. Additionally, we expect the synchronous condition will be more likely to experience a climate of authenticity and argue that shared distress fills psychological needs that underpin flourishing. Our findings confirm that the link between the intervention condition and flourishing is mediated by a climate of authenticity and that students in the synchronous condition reported a higher climate of authenticity than the asynchronous condition post-program. These results reveal that sharing distress is an important contextual process mechanism that promotes flourishing.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".