Learning Leaders: Teaching and Learning Frameworks in Flux Impacted by the Global Pandemic
Bibliographic record
Abstract
This article builds on the work of EDUsummIT2019’s thematic working group 2 (TWG2) focus on “Learning as Learning Leaders: How does leadership for learning emerge beyond the traditional teaching models?” Using the well-established theoretical frameworks of Entwistle (1987) and Shulman (1987) the most significant influences on how learning leaders need to adjust to accommodate the dramatic increase in remote online learning are identified. The major influences include learners’ previous knowledge, self-confidence, abilities and motives, and changes between learning initiated by teachers and that by learners. COVID-19 has caused a massive upskilling of people in all facets of society from children to grandparents, from media to consumers, and from policy makers to practitioners. None of the alignments nor factors identified in this article are static and learning leaders need to perpetually reconsider the factors identified to achieve successful learning outcomes. The ongoing challenges for educators in this changing world are in a permanent state of flux with an increasing IT literate society across all formal and informal sectors of education.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".