Board 352: Preparing Mechanical Engineering Students for Industry 4.0: an Internet of Things Course
Bibliographic record
Abstract
Smart products can sense their environment, analyze lots of data (big data), and connect to the Internet to allow exchanging data.These capabilities are known as the Internet of Things (IoT) technologies.As they become ubiquitous, smart products provide enormous opportunities for scientists and engineers to invent new products and influence interconnected systems of vast scale.Mechanical engineers will play a significant role in innovating and designing smart products and manufacturing systems of the Industry 4.0 revolution.However, the current mechanical engineering curriculum has not kept pace.In this paper, we present details of a new IoT course for mechanical engineering students.The course contains active learning and project-based learning components.Specifically, a smart flower pot device was integrated into the lectures of the course as an active learning platform.In addition, the course incorporates team projects involving design of smart products.The agile method, often used in software development companies, is introduced to the mechanical engineering students to manage their project development process.The paper concludes with assessment details from the first offering of the new course.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".