Open Data Transcripts from the Study: Exploring Educators Experiences Implementing Open Educational Practices
Bibliographic record
Abstract
This dataset contains eight transcripts from interviews conducted with educators as part of a PhD study exploring open educational practices. The research focuses on how educators are using openly accessible sources of knowledge and open-source tools in ways that impact their pedagogical designs. Using a phenomenological approach with self-identifying open education practitioners, I explore how open educational practices are being actualized in formal higher education and impacting learning design. Specifically, I examine how educators are bringing elements of openness into their everyday teaching and learning practice using educational technologies. I draw upon Giddens (1986) structuration theory, further developed for use in technology adoption research most notably by DeSanctis and Poole (1994) and Orlikowski (2000). This approach positions technologies as being continually socially constructed, interpreted, and put into practice. In an organizational context, the use of technology is intrinsically linked with institutional properties, rules and norms, as well as individual perceptions and knowledge. The findings suggest that open educational practices represents an emerging form of learning design, which draws from existing models of constructivist and networked pedagogy. Open technologies are being used to support and enable active learning experiences, presenting and sharing learners work in real-time, allowing for formative feedback, peer review, and ultimately, promoting community-engaged coursework. By designing learning in this way, faculty offer learners an opportunity to consider and practice developing themselves as public citizens and develop the knowledge and literacies for working 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 widely, demonstrates to them that their work has inherent value beyond the course and can be an opportunity to engage with their community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.012 | 0.005 |
| 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".