Guest Editorial: eLearning in the Caribbean
Bibliographic record
Abstract
This special issue of IJEDICT focuses on the application of eLearning in the Caribbean and presents articles by educators in the region who were participants in eLearning 2009, an international conference organized by the University of the West Indies (UWI) and held in Port of Spain, Trinidad from July 8-11, 2009.Research on eLearning in the Caribbean is of particular importance as higher education institutions in the region look toward the increasing use of Information and Communication Technologies (ICTs) to increase access to tertiary education.The diversity and the geographical separation of the countries in the region, together with a lack of resources, have made access particularly challenging.The Caribbean can be defined geographically as a chain of islands forming a broad arc or crescent, which extends from the Bahamas and Cuba in the north, to Trinidad in the South.The islands making up this chain include Cuba, Haiti/Dominican Republic, Peurto Rico and Jamaica, Argentina, Antigua and Barbuda, the United States Virgin Islands and the British Virgin Islands, Montserrat, Dominica, Guadeloupe, Martinique, St Lucia, St Vincent, Grenada, Barbados and Trinidad and Tobago.Many also consider Guyana and Belize part of the Caribbean, even though they are mainland States.ELearn 2009 was intended to share experiences of those educators in the region who have been using ICTs in exemplary ways, in spite of the many challenges faced.More specifically, it was intended to provide a forum for the UWI to demonstrate the organization's progress, understanding and creativity in using technology.The institution celebrated sixty years of existence in 2009 and today has a presence in sixteen English-speaking countries in the regionThe conference attracted approximately 200 attendees, and there were varied presentations, with conference participants coming from Trinidad and Tobago, Canada, the United Kingdom, Hong Kong, Missouri, Virginia, Jamaica, Puerto Rico, Barbados, and Guyana.The struggle to find innovative ways to apply technology to increase access and bridge the digital divide was a recurrent theme of the conference and the articles ranged in focus from pedagogical practices using eLearning, to best practices in the eLearning and the business environment, professional development, enabling environments and innovative eLearning.The articles selected for this issue all reflect work being done at the UWI but span a range of areas from pedagogy to infrastructure and software development, and all provide examples of the possibilities of eLearning in developing contexts.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".