Beyond content delivery: harnessing emotional intelligence for community building in fully online digital spaces
Bibliographic record
Abstract
Abstract The onset of the pandemic catalyzed a paradigm shift in educational methodologies, bringing various forms, such as hybrid, distance, and fully online models, into focus. The following study explores the affective domain in online learning, focusing on how emotions, facial expressions, and body language influence engagement and support community building in fully online learning environments. This research explores the role of emotional intelligence in Fully Online Learning Communities (FOLC) and examines the impact of positive and negative emotions on interpersonal engagement and participation. Findings indicate positive emotions to be closely linked to increased engagement and active participation. The study also highlights the importance of exploring body language in digital learning environments and addresses challenges posed by technological barriers in fully online learning spaces. Emotional intelligence is pivotal in online learning and community building, emphasizing the need to understand how to create emotionally supportive digital learning environments. Outcomes indicate a need for future research to focus on understanding the role of cultural dimensions in supporting learner agency and community building in the fully online learning context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".