Bibliographic record
Abstract
Rapid e-learning (REL) is a phrase in common use since 2003. This article defines REL, describes types of REL authoring tools, discusses management and instructional issues surrounding REL in corporate and academic settings, and summarizes the experience of the National University of Singapore (NUS), an early adopter of the concept of REL since 2004. Almost all current literature on the topic focuses on REL applications in corporate e-learning. There is very little academic research into issues surrounding REL because this is a recent development. At this stage of implementation of REL, the literature on the topic is limited. The following three definitions are commonly used: 1. Josh Bersin defined REL as a category of online training content, which can be developed in weeks, can be authored by subject matter experts (SMEs), and maintains instructional focus and quality (Bersin & De Vries, 2004). REL tools leverage on common software such as PowerPoint and then convert that to Flash or other formats for Web delivery with options to add audio and simple quiz. Content is published, edited, and republished by the SMEs with little or no assistance. 2. Patti Shank, President of Learning Peaks, broadened the definition to include rapid instructional design, development, deployment, and evaluation (Shank, 2006). REL is no longer just synonymous to the rapid authoring and development of content, but also to the streamlining of the entire project management process and production cycle. 3. Another possible definition of Rapid E-learning is when the phrase is used to indicate how rapidly e-learning is being adopted or embraced by an organization. (Tan, Lee & Goh, 2004). The definitions by Bersin & Associates and Patti Shank, which include process and product, are widely accepted as the main definitions of REL.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.018 |
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".