Drivers of Digital Realities for Ongoing Teacher Professional Learning
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.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".