Developing Coding Structures For Becoming Affect-Savy in the Fully Online Community Model
Bibliographic record
Abstract
This study aims to provide support for the efficacy of the Fully Online Learning Community (FOLC) Model by examining communication between participants within a series of recorded online focus groups and by investigating the behaviours that are undertaken by participants. A coding system based on body language expressions is proposed as an outcome of this study and the affective domain of the participants is analyzed through facial expressions, body language and content (words) employed. Findings suggest that affects (emotions) have a preeminent role in the social presence in FOLC environments. Positive emotions are easier to detect as individuals exhibit them without masking, with some possible exceptions arising from personal dispositions and cultural inferences. Negative emotions can also be detected through a combination of facial expressions and body language coding. However, findings were not consistent for determining sadness and surprise states and further studies will have to explore ways to differentiate these affects from others. The instigations set forward by the participants and affective responses to the behaviours of instigators provided support for the empirical study about the efficacy of facilitation and interactions within fully online learning environments.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".