Learning Leaders: Teaching and Learning Frameworks in Flux Impacted by the Global Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it