Analysis of Stroking Motions in Drawing by Experts for Development of a Learning Support System
Bibliographic record
Abstract
Illustrators who have some drawing experience can sometimes draw an ideal stroke repeatedly. However, they cannot always reproduce an ideal stroke that they have drawn, because they do not understand how they moved their own hands to draw it. In order to solve this problem, a new learning support system based on presenting the ideal stroking motion of the learner as a teacher's motion is needed so that learners can enhance their own drawing skills. In order to clarify the differences in motion between good and bad drawing strokes as the first step in the development of such a system, this paper analyzes the stroking motions of experts in the process of drawing circles. Several features are exploited, namely, the pen speed, the pen pressure, the time required to draw each quarter circle, and the movement of the hand. With these features, the accuracy of classification using machine learning is 67% on average. This means that stroke speed, pen pressure, and stroke rhythm (which is the specific pattern of changes in speed) may be useful to distinguish between good and bad strokes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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 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".