Facilitating interpersonal interaction and learning online: Linking theory and practice
Bibliographic record
Abstract
INTRODUCTION: An earlier study of physicians' perceptions of interactive online learning showed that these were shaped both by program design and quality and the quality and quantity of interpersonal interaction. We explore instructor roles in enhancing online learning through interpersonal interaction and the learning theories that inform these. METHODS: This was a qualitative study using focus groups and interviews. Using purposive sampling, 50 physicians were recruited based on their experience with interactive online CME and face-to-face CME. Qualitative thematic and interpretive analysis was used. RESULTS: Two facilitation roles appeared key: creating a comfortable learning environment and enhancing the educational value of electronic discussions. Comfort developed gradually, and specific interventions like facilitating introductions and sharing experiences in a friendly, informative manner were helpful. As in facilitating effective small-group learning, instructors' thoughtful use of techniques that facilitated constructive interaction based on learner's needs and practice demands contributed to the educational value of interpersonal interactions. DISCUSSION: Facilitators require enhanced skills to engage learners in meaningful interaction and to overcome the transactional distance of online learning. The use of learning theories, including behavioral, cognitive, social, humanistic, and constructivist, can strengthen the educational design and facilitation of online programs. Preparation for online facilitation should include instruction in the roles and techniques required and the theories that inform them.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".