A Team of Instructors’ Use of Social Presence, Teaching Presence, and Attitudinal Dissonance Strategies: An Animal Behaviour and Welfare MOOC
Bibliographic record
Abstract
<p class="3">This case study examined a team of instructors’ use of social presence, teaching presence, and attitudinal dissonance in a Massive Online Open Course (MOOC) on Animal Behaviour and Welfare (ABW), designed to facilitate attitudinal learning. The study reviewed a team of six instructors’ use of social presence and teaching presence by applying the Community of Inquiry (CoI) framework, as well as the establishment of attitudinal dissonance within the announcements and discussion forums. The instructors entered the MOOC as a collaborative facilitation team and created a highly balanced manner of communication and positive atmosphere within the course. The instructional design focused on creating an informative and knowledgeable network of global learners that would agree that animal welfare was a critical social issue in today’s society. These course goals and facilitation intentions were demonstrated through a high number of social and teaching presence indicators, with a significant use of all social presence, teaching presence, and attitudinal dissonance categories in evidence. The results present a review of an instructional team’s facilitation that focused on shaping attitudes about the topic of animal behaviour and welfare within a MOOC. We conclude by providing insights into instructional design and facilitation of MOOCs in general or attitudinal learning 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 | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".