Mobile learning in engineering using smartphones: Two examples in acoustics and thermal courses
Bibliographic record
Abstract
Mobile learning is usually seen as a form of distance education where learners use portable devices such as mobile phones to learn anywhere and anytime. The portability that mobile devices provide allows for learning anywhere but also opens the possibility for field testing. Smartphones are indeed one of the common traits of today's students, who can't imagine life without these tools, which are almost seen as an extension of themselves. These communication tools are also working tools, and we want to highlight in this project the possibility of taking advantage of this technology as part of student training. We aim to introduce the smartphone as support for laboratory or experiential work support through a project entitled ‘Flexible and applied mechanical and building engineering laboratories involving smartphones and digital resources.’ Even though it is still a work in progress, this communication presents concrete examples of how smartphones, applications, and short experiments or analyses can enhance learning in two undergraduate courses: ‘Acoustics and noise control’ and ‘Thermal Engineering.’ By leveraging these tools, we aim to create a practical, engaging learning environment that fosters inclusivity and diversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".