A Didactic Activity for Introducing Design and Optimization of Experiments Assisted by Revised Bloom's Taxonomy
Bibliographic record
Abstract
The methodology used in this study was action-research and the considered activity is been applied successfully for at least five years to undergraduate and to master classes for Industrial Engineering students in Brazil. It showed a significant result in both cases, providing the basis for the deepening in the subject in further lessons. Therefore, the main purpose of this paper is to present this didactic activity that uses a statistical software to introduce the subject Design of Experiments (DoE) starting from a simple process of everyday life. Despite DoE in Brazil being emphasized in Industrial Engineering, this issue is extremely relevant in all areas of research where there is experimentation. The activity’s idea was to make students able to interact and understand the concepts of planning and optimizing experiments in a natural and intuitive way by following seven steps proposed in the literature: 1. Recognition and statement of the problem, 2. Choice of factors, levels, and ranges, 3. Selection of the response variable, 4. Choice of experimental design, 5. Conduction of experiment, 6. Statistical data analysis, and 7. Conclusions and recommendations. In order to confirm and strengthen the learning process, some exercises are then offered to students. To ensure the effectiveness, the exercises were designed based on the new version of the Taxonomy of Educational Objectives, also called Revised Bloom's Taxonomy. This proposed exercises discuss the content at all levels of the cognitive domain.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".