Open Pedagogy and Transdisciplinary Thinking: Making Connections Through a Visual Artefact Self- Study
Bibliographic record
Abstract
The examination of teacher educators’ own practices through self-study research has been well established and self-study aligns with the growing interest in open educational resources (OER) and open pedagogy. This research used a self-study method of a Science, Technology, Engineering, Art, and Mathematics (STEAM) OER project, Form and Function(s): Sustainable Design meets Computational Thinking. Two research questions were pursued: How do open pedagogy attributes contribute to a transdisciplinary STEAM OER pedagogical stance? And how can one apply visual artifact self-study as intentional critical friends to examine professional value and to enhance pedagogical self-understanding? The researcher analyzed visual artifacts of created and documented images that supported the process of her interrogations of transdisciplinary curriculum development and open pedagogy. The sites and modalities of the artifacts were questioned and answers recorded using a critical visual methodology. Klein’s (2008, 2018) transdisciplinary thinking and the eight attributes of Hegarty’s (2015) open pedagogy frame the interrogation of the images and the connections made to curriculum theorizing. The self-study provides conclusions to the role of visual artifacts when conceptualizing the gestalt of complex ideas and relations. The self-study provides warranted assertions for open educators and researchers interested in the practices of transdisciplinary, open curricular and pedagogical processes alongside the eight attributes of open pedagogy, and the role of critical self-reflection.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".