Turning the digital divide into digital dividends through free content and open networks: WikiEducator Learning4Content (L4C) Initiative
Bibliographic record
Abstract
In today’s world, where tuition fees continue to rise rapidly and the demand for higher education increases in both the developing and developed world, it is important to find additional and alternative learning passage ways, learners can afford. Traditional education as we have known it has begun to change, allowing for new parallel learning opportunities to take shape and avenues to open up. This paper describes the world’s largest online training initiative in open education, teaching wiki technology online to educators in the formal education sector worldwide but not limited to. “WikiEducator” founded in 2006, operated with funding support by the William and Flora Hewlett Foundation (WFHF) and under the auspices of the Commonwealth of Learning (COL), an intergovernmental organization created by Commonwealth Heads of Government, to encourage the development and sharing of open learning and distance education knowledge, resources and technology. In May 2009, it became its own entity residing under the Otago Polytechnic’s International Centre for Open Education Resources under the auspices of the Open Education Resource Foundation (OERF) in Dunedin, New Zealand, where it is still today. WikiEducator’s flagship, the Learning4Content (L4C) project builds capacity among global educators by teaching wiki technology to newcomers in open education and experts alike, and asks participant to create open content on WikiEducator, to contribute towards WikiEducator’s strategic objectives, in exchange for the one free training opportunity received. The success of the L4C project provided the basis for WikiEducator reaching its target figures of teaching 2500 educators wiki skills in three year, two years in advance and was the reason why large number of newbies and experts alike joined the project. Even though most learners make users of the offered free learning opportunities through the L4C project, there are learners in today’s world who will never have the opportunity to learn online or even have access to computers. WikiEducator developed a feature called “wiki-to-print” which allows you to select and combine free and open WikiEducator content into a book that can be printed out and used offline. This provides an opportunity to reach the unreached to gain access to knowledge and information. The paper will take you through the different development stages and outcomes and is the world’s largest attempt to build wiki skills among global educators.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".