Developing the Learning Achievement of Motion and Force and Analytical Thinking of Grade 8 Students Using Integrated STEM Education and Inquiry-Based Learning
Bibliographic record
Abstract
This Study aim to investigate the effects of integrated inquiry-based and STEM education learning management on Thai grade 8 students’ analytical thinking and learning achievement. The research employed a one-group experimental design with 37 grade 8 students from a public school in Khon Kaen Province, Thailand, selected through purposive sampling. The instruments used in this study included an inquiry-based STEM education integrated learning management plan, a learning achievement test on motion and force, and an analytical thinking test. Data were collected through pretests and posttests on learning achievement and analytical thinking, and ongoing assessments during the learning activities. The data were analyzed using percentage, mean score, standard deviation, effectiveness index (E1/E2), and a paired samples t-test. The results indicated that the overall effectiveness of the learning management, calculated as the E1/E2 ratio, was 77.20/77.57, meeting the predetermined criteria of 75/75. Furthermore, significant increases in analytical thinking and learning achievement were observed at the statistical level of 0.5. These findings support the effectiveness of integrating inquiry-based learning with STEM education in enhancing students’ analytical thinking and learning achievement, contributing valuable insights to educational practices in science education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".