Instructor’s Use of Social Presence, Teaching Presence, and Attitudinal Dissonance: A Case Study of an Attitudinal Change MOOC
Bibliographic record
Abstract
<p class="2">This study examines a MOOC instructor’s use of social presence, teaching presence, and dissonance for attitudinal change in a MOOC on Human Trafficking, designed to promote attitudinal change. Researchers explored the MOOC instructor’s use of social presence and teaching presence, using the Community of Inquiry (CoI) framework as a lens, and examined the facilitation of attitudinal dissonance within the discussion forum, announcements and blog postings in the course. The instructor entered the MOOC with the idea of serving as a co-participant and a facilitation choice was made to address the issue of multiple perspectives and experiences. The instructional design focused on establishing a collaborative community of learners and this was demonstrated through a high number of social presence indicators but with significant use of all three areas in evidence. Findings present a detailed examination of instructor strategies in a MOOC designed to focus on the establishment of a collaborative learning community and can inform future instructional design and instruction of MOOCs in general and MOOCs for attitudinal change specifically.</p>
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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 | 0.007 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".