Drivers of Digital Realities for Ongoing Teacher Professional Learning
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
Abstract In an era marked by the widespread use of digital technology, educators face the need to constantly learn and develop their own new literacies for the information era, as well as their competencies to teach and apply best practices using technologies. This paper underscores the vital role of ongoing teacher professional learning (OTPL) with a focus on reflective practices and pedagogical reasoning and action (PR&A) in shaping education quality and equity. Examining three key drivers of educational transformation—big data and learning analytics, Artificial Intelligence (AI), and shifting teacher identities—the paper explores their overall impact on teacher practices. This paper emphasizes technology as a crucial boundary object, a catalyst of educational transformation, when used to foster communication and professional growth. To this end, three boundary objects are identified, namely dashboards, AI-driven professional learning environments, and digital communities of practice. These tools illustrate technology’s capacity to mediate relationships between transformative educational drivers and teacher practices, offering a pathway to navigate shifting perspectives on OTPL. With a theoretical foundation in equitable education, the paper provides insights into the intricate relationship between boundary objects and evolving educational dynamics. It highlights technology's pivotal role in achieving both quality and equitable education in the contemporary educational landscape. It presents a nuanced understanding of how specific tools may contribute to effective OTPL amid rapid educational transformations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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