Embedding physics into technology: infrared thermography and building inspection as a teaching tool — a new participated strategy approach to the physics of heat transfer and energy saving for professional schools
Bibliographic record
Abstract
We describe an inquiry-based path about heat conduction as part of a multidisciplinary project on energy saving in a professional school in a province close to Milan, Italy. The teaching–learning process dealt with heat losses in buildings detected with a thermal camera. Three consecutive activities were implemented: direct detection by the students of heat leakages due to thermal bridges in the school structure; simple standard technology laboratory activities on heat transfer, planned and performed by the students themselves; and finally a series of guided laboratory experiences with a thermal camera, to develop and clarify the previous lab activities on thermal conductivity. Key motivations of the project were creating a link between the study of thermodynamics and its application to the “real” world; increasing students’ motivation by using an Inquiry Based Science Education (IBSE) approach; and studying if and how the “infusion” of a cutting-edge, and therefore science-attracting, technology (thermography) might foster the teaching–learning process, thus becoming a concrete cognitive tool promoting the students’ approach to the scientific methodology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".