TEACHING EXPERIMENTAL DESIGN METHODOLOGY USING TOYS AND SOFTWARE TOOLS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most engineers do not have adequate training on how to deal with the design and analysis of multi-factored experiments. Yet, both ABET and CEAB clearly state that an engineering graduate should be able to conduct, analyse, and interpret the results of such experiments. It is now recognized that the factorial approach is the correct and scientific approach in conducting multi-factored experiments. The question is: how do we teach the proper experimental designs and principles to students in a course in such a way that is easy to learn, fun, exciting, competitive, and memorable? In this paper, an approach that uses specially designed toys and software that allow students to conduct multi-factored experiments, analyze the data collected, and do follow-up experiments, is described. Several physical and software toys are described and how they are used in a course on experimental design for engineers will be presented. The software used to analyze and present the data is also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it