A Learning Management System Enhanced with Internet of Things Applications
Bibliographic record
Abstract
A breakthrough in the development of online learning occurred with the utilization of Learning Management Systems (LMS) as a tool for creating, distributing, tracking, and managing various types of educational and training material. Since the appearance of the first LMS, major technological enhancements transformed this tool into a powerful application for managing curriculum, providing rich-content courseware, assessment and evaluation, and dynamic collaboration. With several current research fields targeting various technologies related to the LMS, the future promises many changes in its structure, operations, and implementation. The most important technology that is expected to transform many future aspects is the Internet of Things (IoT). In this paper, we provide a framework for a future LMS enhanced by IoT capabilities. We outline several elements of the LMS that will be affected by IoT, and the expected enhancements and changes that IoT will bring to the LMS functionalities. The framework presented for the IoT-enhanced LMS constitutes the main component of a three year research project that is being conducted at the Arts, Sciences, and Technology University (AUL). In this paper, we illustrate the main parts of this project and the implementation plan of each part, including the prospected outcomes and benefits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".