A 3D printing Short Course: A Case Study for Applications in the Geoscience Teaching and Communication for Specialists and Non-experts
Bibliographic record
Abstract
3D printing developed as a prototyping method in the early 1980s, yet it is considered as a 21st century technology for transforming digital models into tangible objects. 3D printing has recently become a critical tool in the geoscience research, education, and technical communication due to the expansion of the market for 3D printers and materials. 3D printing changes the perception of how we interact with our data and how we explain our science to non-experts, researchers, educators, and stakeholders. Hence, a one-day short course was designed and delivered to a group of professors, students, postdoctoral fellows, and technical staff to present the application of 3D printing in teaching and communication concepts in the geoscience. This case study was aimed at evaluating how a diverse group of participants with geoscience and engineering background and no prior experience with computer-aided modeling (CAD) or 3D printing could understand the principles of different 3D printing techniques and apply these methods in their respective disciplines. In addition, the course evaluation questionnaire allowed us to assess human perception of tangible and digital models and to demonstrate the effectiveness of 3D printing in data communication. The course involved five modules: 1) an introduction lecture on the 3D printing methods and materials; 2) an individual CAD modeling exercise; 3) a tour to 3D printing facilities with hands-on experience on model processing; 4) a tour to experimentation facilities where 3D-printed models were tested; and 5) group activities based on the examples of how to apply 3D printing in the current or future geoscience research and teaching. The participants had a unique opportunity to create a digital design at the beginning of the course using CAD software, analyze it and 3D print the final model at the end of the course. While this course helped the students understand how rendering algorithms could be used as a learning aid, educators gained experience in rapid preparation of visual aids for teaching, and researchers gained skills on the integration of the digital datasets with 3D-printed models to support societal and technical objectives.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".