Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education
Bibliographic record
Abstract
Open educational practices (OEP) is a broad descriptor of practices that include the creation, use, and reuse of open educational resources (OER) as well as open pedagogies and open sharing of teaching practices. As compared with OER, there has been little empirical research on individual educators’ use of OEP for teaching in higher education. This research study addresses that gap, exploring the digital and pedagogical strategies of a diverse group of university educators, focusing on whether, why, and how they use OEP for teaching. The study was conducted at one Irish university; semi-structured interviews were carried out with educators across multiple disciplines. Only a minority of educators used OEP. Using constructivist grounded theory, a model of the concept “Using OEP for teaching” was constructed showing four dimensions shared by open educators: balancing privacy and openness, developing digital literacies, valuing social learning, and challenging traditional teaching role expectations. The use of OEP by educators is complex, personal, and contextual; it is also continually negotiated. These findings suggest that research-informed policies and collaborative and critical approaches to openness are required to support staff, students, and learning in an increasingly complex higher education environment.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".