IoT-based Experiential E-Learning Platform (EELP) for Online and Blended Courses
Bibliographic record
Abstract
This article describes the design and implementation of a remote laboratory for learning embedded systems using Internet of Things (IoT) technology. The main objective of this remote laboratory is to enhance the learning on sensors, actuators, interfacing, and real-programing in engineering education and dealing with industrial automation and automotive applications. With the growing IoT platforms, the telepresence of learners in physical lab facilities implemented using a webcam, telepresence controllers, and graphical user interface (GUI). In the developed platform, learners will be able to practice hardware programming via remote access to lab facilities and observe the sensor's and actuator's functionalities. This platform allows the learner to practice in course materials anywhere and anytime using smartphones, tablets, and laptops. Therefore, learners can also practice control algorithms mainly used in automation and automotive industries and then deploy them on the existing industrial platform. A set of student-center laboratory activities has been developed with a pedagogical approach based on Kolb's Experiential Learning Theory as a complement to the proposed venue. The developed platform is a very low-cost platform integrated into expensive available platforms and available remotely.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".