Validity of PBEST Learning Model: An Innovative Learning to Improve Creative Thinking Skill and Entrepreneurial Science Thinking
Bibliographic record
Abstract
Based on the results of previous studies, evidence was acquired indicating that the creative thinking abilities of pupils in various locations of Indonesia still require improvement. Creative and innovative thinking in science learning integrated with entrepreneurship produces entrepreneurial science thinking. Learning interventions are needed to develop competitive graduates who can face challenges and rapid changes in the 21st century. This research aims to validate the Project Based Entrepreneurial Science Thinking (PBEST) learning model. The educational development research design used is the validation studies design, which tests two criteria, namely testing content validity (which is also called relevance) and construct validity (which is also called consistency). This validation involves three experts in science education, and the validation instrument uses a validation sheet. The research and data analysis indicate that the PBEST learning model consistently produces highly relevant results that meet rigorous validity and reliability standards (with a percentage of agreement ≥ 75%). The PBEST learning model consists of four stages, namely: (1) Observe and thinking project, (2) Design project, (3) Monitoring and evaluation project, (4) Economic value. The validation of the implementation supporting learning tools confirms the validity and reliability of the semester learning plans, lecture program units, student worksheets, student books, creative thinking skill tests, and entrepreneurial science thinking tests. The PBEST learning paradigm is applicable for enhancing both creative thinking skills and entrepreneurial science thinking.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".