Open Education and Learning Design: Open Pedagogy in Praxis
Bibliographic record
Abstract
Beyond providing alternatives to traditional learning resources, there exists a gap in the literature in understanding how openness is impacting teaching and learning in higher education. This paper explores the ways in which educators describe how open education is impacting their pedagogical designs. Using a phenomenological approach with self-identifying open education practitioners, we explore how open educational practices (OEP) are being actualised in formal higher education in the context of British Columbia (BC), Canada. The findings suggest that OEP represent an emerging form of learning design, which draws from existing models of constructivist and networked pedagogy, while using the affordances of open tools and content to create and share learning in novel ways. Faculty members report finding ways to use open approaches and technologies to support and enable active learning experiences, present and share learners’ work in real-time, support formative feedback, peer review, and, ultimately, promote community-engaged coursework. By designing learning in this way, faculty members offer learners an opportunity to consider and practise developing themselves as public citizens, develop their knowledge and literacies for working appropriately with copyright and controlling access to their online contributions, while presenting options for extending some of those rights to others. Inviting learners to share their work more widely, demonstrates to them that their work has inherent value beyond the course and can be an opportunity for them to engage directly with their community.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".