GELO and GreX: A framework and dashboard to investigate technology competency and culture
Bibliographic record
Abstract
Participation in 4th Industrial Revolution society is increasingly dependent upon competencies related to the use of digital technologies for a wide variety of purposes. A person’s competence in the use of digital technologies has implications for a wide variety of contexts and situations, including learning in physical as well as virtual spaces, career choices and employability, digital citizenship, cultural orientations and values, and even democracy (Erstad, 2010). This workshop will provide an overview of the Global Educational Learning Observatory (GELO) project and invite participants to experience a variety of self-assessment tools accessed through the customizable dashboard, the Global Readiness Explorer (GREx). The GELO project attempts to provide a framework for an international network of institutions utilizing data-driven evidence to inform evolving best practices for online and mobile learning. To achieve this, the project (i) assembles a nucleus of formal educational institutions, (ii) constructs the necessary tools to extend research on formal learning models, and (iii) reaches into the workplace as well as other 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 the tools that can be implemented within it. These tools are designed to give individuals, organizations, and institutions the means to construct complex profiles that can be used to identify gaps in competency attainment and development. Through small group activities, participants will examine and discuss the various tool suites in the GREx including the digital learning competency profiler (DCP); the fully online learning community survey instrument (FOLCS); the Personal Cultural Orientation Scale (PCOS) and others. In this workshop, participants will choose a self-assessment instrument to participate in and then discuss their experience with a focus on improving the GREx tool suite for global use. In addition, participants will examine the GREx for use with their students as a component of determining readiness for moving into fully online learning environments. The workshop will conclude with an explanation of the global educational learning observatory (GELO), and participants will be invited to join this global research network.
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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".