Bridging SoTL and Open Educational Resources/Practices (OER/P) Through Tagore’s Southern Theory in the Era of a Pandemic
Bibliographic record
Abstract
In recent times, while Scholarship of Teaching and Learning (SoTL) and pedagogy enabled by open educational resources/practices (OER/P-enabled pedagogy) have become paramount in higher education, both have hardly been addressed jointly. Given that both aim to adopt, share, and reuse open resources by lecturers and their students in higher education institutions, it would have been expected that both are conjointly used to enhance student learning instead of being used separately. Additionally, while studies are ongoing from different theoretical stances, SoTL and OER/P-enabled pedagogy research are considered separately. Consequently, and through systematic review, the current study aims at bridging SoTL and OER/P-enabled pedagogy through Tagore’s southern theory in the era of a pandemic in the educational setting. Tagore’s southern theory can demonstrate and explain the tacit linkage between SoTL via OER/P. Accordingly, Tagore’s southern theory should be used in broadening and deepening our understanding and thus serve as a guiding principle to approach SoTL and OER/P-enabled pedagogy conjointly. One key implication is that while it is important to conceptualize the global design of teaching and learning, it is vitally important to take account of local histories. Thus, the culturally entrenched connotations and interpretations for inclusive education can be addressed by employing both SoTL and OER/P-enabled pedagogy simultaneously. The hope is that the research assists in grounding the future of SoTL via OER/P-enabled pedagogy exploration.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".