Pedagogical Transformation and Teacher Learning for Knowledge Building: Turning COVID-19 Challenges into Opportunities
Bibliographic record
Abstract
This paper reports on the continual effort of the Knowledge Building Community (KBC) connecting teachers within and across schools for knowledge creation and community building during the COVID-19 disruptions. During this crisis, schools around the world are challenged with the issues of implementing online learning. Three areas of misalignment were identified: disjoint in learning with home-school separation, piecemeal technologies to mimic physical teaching, and disconnect between teacher professional development and classroom practices and we discussed emerging realignment efforts for transformative learning. Through analyzing the three case examples of how teachers responded to COVID-19 challenges in inter-related areas of curriculum, pedagogy, technology and community, we identified several themes on emerging alignments conducive for transformative pedagogy and technology through community advancement. These themes include: innovating practice around the centrality of ideas; perceiving knowledge building as pervasive; transformative use of technology, and symmetrical advancement of knowledge. These case examples show that in these disruptive times, the teachers were more actively building new practices supported by community dynamics and systemic processes of the KBC. Consequently, the interactions between stakeholders shifted from disjointed relations in different hierarchical levels to a networked community of people, ideas, and resources, and teachers continually advancing their knowledge-building practice in these challenging times.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.044 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".