Development of Ceramics Creative Process with Community-based Learning and Constructionism for Undergraduate Students
Bibliographic record
Abstract
The purposes of this research were 1) to develop a ceramics creative process with community-based learning (CBL) and constructionism for undergraduate students; 2) to evaluate students’ ceramic work with CBL and constructionism, and 3) to examine the community’s and students’ satisfactions towards CBL and constructionism. The research sample was selected by purposive sampling method to obtain 40 undergraduate students of Uttaradit Rajabhat University who enrolled in the Course: Local Ceramics, semester 1/2016, and 6 community instructors. Data analysis was presented by mean and standard deviation. The findings suggested that the ceramics creative process with CBL and constructionism for undergraduate students involved 5 steps, namely: 1) community survey/selection and exploring community data; 2) preparation; 3) hands-on practice to create ceramic works; 4) presentation of ceramic works; and 5) evaluation. The experimental result of the ceramics creative process with CBL and constructionism being constructed by the author revealed that the students’ knowledge and understanding of ceramics creative process displayed overall mean at a very high level ( = 3.70, S.D. = 0.26; = 3.72, S.D. = 0.16, respectively). The satisfaction towards CBL and constructionism were at a highest level in overall for the community and a high level in overall for students (= 4.67, S.D. = 0.27; = 3.85, S.D. = 0.34, respectively).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".