Use of a Low-cost, Open-source Universal Mechanical Testing Machine in an Introductory Materials Science Course
Bibliographic record
Abstract
Abstract Title: Use of a Low-Cost, Open Source Universal Mechanical Testing Machine in an Introductory Materials Science Course - The recent paradigm shift in engineering education towards more active learning has prompted careful consideration of teaching methods both in lectures and in the laboratory [1]. Laboratory work is recognized as a crucial part of the undergraduate engineering experience, many instructors seek to further enhance the delivery of this critical course component [2-4]. Growing issues with the costs of lab equipment, space, and coordinating the use of existing equipment have limited students’ opportunities to interact with authentic equipment consistent with current engineering practice. - A low-cost, open source modular universal mechanical testing kit (UMTK) has been developed and built [5]. With easily replaceable end effectors, the UMTK can perform tensile, compression and bending tests on small instructional lab sized samples. - The UMTK will be used in lab sessions of an introductory materials science course for first year engineering students at the INSTITUTION REDACTED. Four treatment groups will each receive the same initial didactic introduction to materials testing. Following this, the groups will proceed as follows: the first group will operate the UMTKs, the second group will operate a higher cost, commercially available bench-top mechanical tester, the third group will receive a traditional didactic tutorial with an instructor explaining the concepts of mechanical testing, while the final group will be a control group given unrelated hands-on activities to complete. - Pre and post tests will be conducted on the students’ understanding of the corresponding topic, to determine how their performance changes with the materials delivered in different ways. A survey will also be administered to the students to collect demographic data as well as subjective preferences. - This study will test the hypothesis that use of the UMTK improves students’ performance in course subject on mechanical testing when compared to either traditional didactic instruction, the use of a commercially available benchtop mechanical tester, or performing unrelated hands-on work for the same time. This study will inform further, larger scale deployment of similar low-cost, open source mechanical testers. [1] M. Prince, "Does Active Learning Work? A Review of the Research", Journal of Engineering Education, vol. 93, no. 3, pp. 223-231, 2004. [2] L. Feisel and A. Rosa, "The Role of the Laboratory in Undergraduate Engineering Education", Journal of Engineering Education, vol. 94, no. 1, pp. 121-130, 2005. [3] J. Ma and J. Nickerson, "Hands-On, Simulated, and Remote Laboratories: A Comparative Literature Review", ACM Computing Surveys, vol. 38, no. 3, 2006. [4] M. Friesen, K. Taylor, and M. Britton, “A Qualitative Study of a Course Trilogy in Biosystems Engineering Design”, Journal of Engineering Education, vol. 94, no. 3, pp. 287-296, July 2005. [5] X. Liu, S. Pajovic, M. Burgers, C. Zhi, “Final Design Specification: The Universal Mechanical Testing Kit”, Capstone Design, Mechanical and Industrial Engineering, University of Toronto, Toronto, 2019.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".