From Cherry Picking to Convergence – Migrating E-Learning Delivery to an LMS (Learning Management System) – the COLeLIO Experience
Bibliographic record
Abstract
The Commonwealth of Learning e-learning for International Organizations (COLeLIO) (www.col.org/colelio) Initiative engages appropriate technology to custom design and deliver just-in-time (JIT) workplace e-learning for adult learners based in field offices and headquarters of international organizations spread all over the world. To ensure reliable and easy access to learning, COLeLIO chooses appropriate technologies to underpin course design, development and delivery taking into consideration bandwidth and access issues. Responding to constantly changing learning environments and learners' needs, eLIO learning materials have evolved from print to digital over the last decade. In recent years, eLIO saw the need to streamline the delivery operation for their 1,000 learners annually, involving 40 tutors and teams of course administrators. Recognizing that the conduits supporting online learning have mushroomed in recent years and that more affordable and robust open source learning management platforms are available to support and sustain online learning for transparency, accountability and quality results, COLeLIO spent 12 months searching for and adapting a technology solution to create a one-stop access to resources, support, discussions, and records for learners, tutors and administrators. This paper captures the story of change management by eLIO and shares some key lessons learned.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".