A Comparative Study of Teaching Process of Presenting Product Sketch Design of Industrial Design Program
Bibliographic record
Abstract
This research studies the teaching process of idea communication for industrial product design sketching. The objective of this research is to make a comparative study on the efficiency of two teaching processes between teaching with detailed information and teaching with conceptual frameworks for groups of students who have different learning aptitudes; which are an aptitude in theoretical subjects or an aptitude in practical subjects. The study also included differences in learning styles of the industrial design program undergraduate students. The researchers came up with an experiment of creating sketch design ideas for a product in which the researchers classified the students’ learning processes from curriculum subjects and academic achievements. The results found that curriculum subjects and students’ learning aptitude can be grouped into two major groups: students who have accumulated scores in theoretical subjects and students who have accumulated scores in practical subjects. These two groups of students have different aptitudes in sketch design idea communication processes: a process of sketching with given detailed information and a process of sketching with given conceptual framework. Although these are different processes, the teaching and learning of these two product design processes have the same objectives: to create design ideas and to support design creativity by using the concept of interaction between the brain, hands and shapes that appear on paper to present the sketch product and to guide the teaching and learning of industrial product design, suitable for students who have different characteristics and help increase their academic achievements.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".