Laboratory as a Service (LaaS): a Novel Paradigm for Developing and Implementing Modular Remote Laboratories
Bibliographic record
Abstract
The increasing adoption of remote laboratories in education along with the shift from eLearning 2.0 towards eLearning 3.0, have demanded several considerations in their implementation and delivery format. In response to these needs, this contribution introduces a novel model, Laboratory as a Service (LaaS), for developing remote laboratories as independent component modules and implementing them as a set of loosely-coupled services to be consumed with a high level of abstraction and virtualization. LaaS aims to tackle the common concurrent challenges in remote laboratories developing and implementation such as inter-institutional sharing, interoperability with other heterogeneous systems, coupling with heterogeneous services and learning objects, difficulty of developing, and standardization. Beyond the academic context, LaaS will facilitate the incorporation of remote laboratories in the ecosystem of the ubiquitous smart things surrounding us, which increases everyday with the approaching Web of Things (WoT) and artificial intelligence era. This, in turn, will create a breeding ground for online control, experimentation, and discovery—in either formal or informal context and with neither temporal nor geographical constraints.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".