INTERCONNECTION OF MATERIALS SCIENCE, 3D PRINTING AND MATHEMATIC IN INTERDISCIPLINARY EDUCATION
Bibliographic record
Abstract
The substantial advantage of 3D printing is the ability to fabricate complex shapes objects from liquid molecules or powder grains which joins or solidifies using computer design files (CAD) to produce a three-dimensional object with material being added together layer by layer. This process is considered as an industrial technology. The most-commonly used 3D printing procedure is a material extrusion technique called fused deposition modelling. The producers of 3D printers have already developed prototypes for education purposes. The importance of the incorporation this printing method in schools is the fact. The learning experience for digital media is becoming a priority in school education. The practical application of this technique can be incorporated into a wide variety of school subjects to simplify the sophisticated theoretical concepts. 3D printing is the example of cooperation within material science and mathematics but this platform is very often not supported by the high school curriculum, but latest trends propose different approaches and make education close to the science achievements and contemporary life. Building lessons plans and project could help students to learn more contemporary achievement in this field. It is new trend to support enthusiastic teachers who want to implement this method of additive manufacturing in education. This paper provides an overview of 3D printing methods and highlights the possibility of their implementation in educational techniques.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".