Connect to Learn: Assemblage of Pedagogies in Higher Education in a Community of Practice
Bibliographic record
Abstract
In this paper, we report on the ways in which technology and scholarship of pedagogy emerge as interconnected within a technology-facilitated community of practice (CoP), for educators within various Faculties of Education in North American universities. The goal of the Community of Practice is to connect with and learn from one another, discussing, and reflecting on different types of pedagogical practices among members who teach in both graduate and teacher education programs in the onsite, blended, and online environments. We share analysis of interviews, and notes from CoP members’ feedback; how the CoP members made sense of their diverse teaching and social learning landscapes as well as emergent joint meanings. The results of the study suggest that the assemblage of new ideas and pedagogies can be enhanced by a relational trust. A highlighted role of technology in enabling communication and collaboration among CoP members is also discussed through the lens of connectivism.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".