Introducing the Global Educational Learning Observatory (GELO) and the Global Readiness Explorer (GREx): A framework and dashboard to investigate tech competence and culture
Bibliographic record
Abstract
The Organization for Economic Co-operation and Development calls for immediate paradigmatic shifts in how educational institutions and the broader society address employment skills. A wide variety of local and global sources, including trade magazines, government sites, and pan-governmental sites, echo this urgent call, recommending increased emphasis on skills development in complex problem solving, critical thinking, creativity, and collaboration. The overarching aim of the Global Educational Learning Observatory (GELO) project is to develop a framework for creating an international network of institutions utilizing data-driven and science-informed evidence of best practices in online and mobile learning, while endeavoring to reach out to a large variety of individuals in formal and informal educational institutions globally. To achieve this, the project aims to (i) assemble a nucleus of formal educational institutions; (ii) construct the necessary tools to extend research on formal learning models; and (iii) reach into workplaces and more public spaces to integrate with informal learning settings. The primary source of data derives from a customizable dashboard, the Global Readiness Explorer (GREx), and a variety of quantitative tools that may be implemented within it. In addition, local initiatives and the development of overarching policies will require the use of qualitative and mixed methodologies for the collection and analysis of textual, video. and other types of artifacts to supplement the quantitative data. In GELO, partner institutions can use the broad range of data types to make decisions about course and professional learning offerings in response to the needs identified through analysis of the data. The results of this project will allow the project team to use digital technology tools to more fully explore informal learning settings around the world as a means of transforming our common understandings of traditional, institutional, and more contemporary, lifelong learning trends.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".