Open Online Laboratory Management System to Promote Standard Practices
Bibliographic record
Abstract
In 2021, the IEEE Education Society created the Virtual Graduate Study Consortium (VGSC), a virtual com-munity of graduate students, faculty members, and industry participants with the goal of promoting the standards developed by the IEEE Education Society and motivating their participation in the currently active standards working groups.As a product of this initiative, multiple activities have been organized during the period 2021-2024. In 2024, a grant from the IEEE Education Society was given to the group to support their activities. This grant provided the opportunity to create a virtual space based on the previously developed online laboratory management system (OLMS) developed in 2019, called SARL (Smart Adaptive Remote Laboratory).SARL OLMS was proposed to present the benefits of having a standard platform capable of providing a robust solution that can be used by laboratory administrators, educators, and learners. The system allows for the design, adaptation and management of laboratory activities and physical laboratory stations based on hardware or simulation.In this OLMS system instance, multiple examples of online laboratory experiments are being deployed following the definitions proposed in the IEEE Standard 1876-2019, to showcase the capabilities of the system and the benefits of the standards.This paper presents the current state of progress of this initiative, presents the process of deployment of a new virtual laboratory online space, and presents the potential benefits for online education and for the standards community.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".