The Development of Scientific Modeling Skill Assessment for Grade 6 Students
Bibliographic record
Abstract
The primary objective of this study was to develop and evaluate a scientific modeling skill assessment for grade 6 students in Thailand. The assessment comprised three test components: a multiple-choice test, a matching test, and a written test, which underwent rigorous evaluation through three trial processes. A total of 370 participants were randomly selected from 29 schools in a province of Thailand using a multi-stage random sampling method. The results demonstrated that the developed assessment exhibited appropriate content validity, difficulty, discrimination, and reliability. Additionally, the assessment provided a means to determine students’ levels of scientific modeling skills, ranging from very high to limited. By addressing the absence of systematic assessments specifically designed for Thai students, this study fills a significant gap in the Thai educational context. The practicality and scalability of the assessment make it suitable for implementation with a larger number of participants. However, it is important to acknowledge the limitation of the study’s relatively small sample size. Future research endeavors should aim to replicate the study with a more diverse and extensive sample to enhance the generalizability of the findings. Overall, this project contributes to the field of scientific modeling skill assessment and offers valuable insights for promoting and evaluating students’ scientific modeling skills in the context of grade 6 education in Thailand.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".