Designing Project-based Learning (PjBL) Activities for Art and Design E-Portfolio Using Fuzzy Delphi Method as a Decision Making
Bibliographic record
Abstract
The present articles introduces the Fuzzy Delphi Method results obtained in the study on designing Project-based Learning (PjBL) activities for art and design courses using Fuzzy Delphi Method (FDM) as a decision-making.This method bases on qualified experts that assures the validity of the collected information. In particular, the confirmation of elements is based on experts opinion and consensus. The consensus survey constructed based on the emergent themes the experts raised from the conducted interview. For this purpose of the study, 22 experts in Project-based learning involved in the interview and responses the consensus survey. The experts participated in this study involves local and international perspectives that contribute to the best idea and practises by their respective's institution. The selection of decision-making will reflect the e-Porfolio users which purposely design for art and design courses. As resulted, the Fuzzy Delphi will interpret the decision-making made by experts to suggest best practices of the pedagogical strategy infused in e-Portfolio system.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".