Theoretical and Methodological Approaches for Investigating Open Educational Practices
Bibliographic record
Abstract
To date, the phenomenon associated with open education in relation to teaching and learning practices remains under-theorized in the literature, which represents both a challenge and opportunity for further research (Bulfin et al., 2013; Howard & Maton, 2011; Knox, 2013; Veletsianos, 2015). There exists an opportunity to develop new theory, as well as to connect the phenomenon to existing theory from education, learning sciences, and pedagogical research. Much of the literature has focused on case studies, strategies for implementation, and broad approaches to institutional change which do not draw upon or develop theory. A significant amount of the empirical work reviewed makes no mention of a theoretical base aside from that of openness as a conceptual framework for considering education. Further, critical studies which examine the pedagogical and educational implications of the use of open educational resources (OER) and engagement in open educational practices (OEP) are even less common (Knox, 2013). In this paper, we share the results of a literature review which investigates both methodological and theoretical approaches used in the available research on open educational practices, with the goal of engaging participants in a critical review of the theoretical and methodological approaches to further advance research in this emerging space.
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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 | 0.086 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".