Fostering Mathematical Proficiency and Creative Thinking Skills in 10th Grade Students Through the 5E Inquiry-Based Learning Approach with Supplementary Media
Bibliographic record
Abstract
This research aims to enhance mathematical proficiency and stimulate creative thinking among 10th-grade students through the implementation of the 5E Inquiry-Based Learning approach with Supplementary Media. The study explores the effectiveness of this pedagogical approach in transforming the learning experience and outcomes in mathematics education. The study focused on comparing students' performance before and after the intervention, as well as investigating their creative thinking abilities in terms of flexibility, originality, fluency, and elaboration. The participants consisted of 10th-grade students from a large-sized school in the northeastern region of Thailand, specifically one classroom comprising 40 students. The tools include Lesson plans, Mathematics Proficiency test and Creative thinking skills test. The results revealed a significant improvement in mathematical proficiency following the implementation of the 5E Inquiry-Based Learning Approach with supplementary media, with a notable increase of 37.25%. Prior to the intervention, students scored an average of 4.64 out of 20, which increased to 12.08 out of 20 post-intervention. Furthermore, the investigation into creative thinking skills indicated that students exhibited a proficient level overall, with an average score of 22.60 out of a maximum of 32 points. The analysis of specific dimensions of creative thinking revealed average scores of 5.60 for Originality, 5.78 for Fluency, 5.63 for Flexibility, and 5.60 for Elaboration.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".