Innovate Integration of guided inquiry learning (GIL) and project-based learning (PjBL) to enhance students’ science literacy
Bibliographic record
Abstract
This study aims to develop and implement an integrated learning model combining Project-Based Learning (PjBL) and Guided Inquiry Learning (GIL) to enhance science literacy in students within the advanced science education course. The study was conducted to students from the Elementary School Teacher Education Program at the Catholic University of Santo Thomas, focusing on the topic of States of Matter. A quasi-experimental design with a pre-test – post-test control group design was employed, where the control group received conventional teaching methods, and the experimental group engaged in the integrated PjBL-GIL model. The results reveal a significant difference in science literacy improvement between the groups. The experimental group, using the integrated PjBL-GIL model, showed a greater improvement (22.4%) compared to the control group (7.7%). Statistical analysis using a two-sample t-test confirmed the significance of this difference, with a t-value of 5.16 and a p-value of 0.000, indicating a statistically significant improvement in the experimental group. The effect size, measured by Cohen's d, was 2.53, indicating a large effect. ANCOVA results showed that even after controlling for baseline differences, the experimental group still demonstrated significantly higher post-test scores (p-value = 0.000). Furthermore, Pearson’s correlation analysis revealed a significant positive relationship between collaboration and science literacy improvement (r = 0.62, p = 0.01), emphasising the importance of collaboration in enhancing learning outcomes. These findings suggest that the integrated PjBL-GIL model is effective in improving critical thinking, problem-solving skills, and the application of scientific concepts. And it is recommended that this model be expanded in science education in Indonesia to further improve the quality of learning and students' science literacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".