Deep in the Hearts of Learners: Insights into the Nature of Online Community
Bibliographic record
Abstract
Recent literature on online learning gives credence to the difficulty inherent in understanding the sense of online community. Quantitative studies especially have concluded with calls for deeper, more intensive explorations into what really happens in online learning environments. In this article the results of an interpretive study conducted among adult learners engaged in online study present an intensive and revealing look into learners’ interaction with online community. Online learning is demanding and unforgiving; in feeling its relentless pull, learners construct their own lines of defense that allow them to complete their studies successfully while maintaining their independence and integrity. La documentation recente sur l’apprentissage en ligne ajoute foi a la difficulte inherente a comprendre le sens de communaute en ligne. Des etudes quantitatives, en particulier, ont conclu sur la necessite de mener des explorations plus approfondies et plus intenses sur ce qui se passe reellement dans les environnements d’apprentissage en ligne. Dans cet article les resultats d’une etude interpretative, menee aupres d’apprenants adultes engages dans l’apprentissage en ligne, presentent une vision intensive et revelatrice sur l’interaction des apprenants avec la communaute en ligne. L’apprentissage en ligne est eprouvant et impitoyable. Les apprenants, en ressentant la pression incessante, construisent leurs propres barrieres de defense ce qui leur permet de completer leurs etudes avec succes tout en maintenant leur independance et leur integrite.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".