Implementation of Guided Inquiry Learning Model to Improve Understanding Physics Concepts and Critical Thinking Skill of Vocational High School Students
Bibliographic record
Abstract
This research aims to implement the guided inquiry learning model to improve the understanding of concepts and critical thinking skills of Vocational High School (SMK) students. The quasi-experiment method with the randomized pretest-posttest control group design was used to obtain data from students of one of the Vocational High Schools (SMK) in Bandung, West Java, Indonesia. The sample consisted of 28 experiment and control class students that carried out a guided inquiry and conventional learning, respectively. The result shows an average increase in understanding of physics concepts and critical thinking skills of the experimental class by 0.71 (high category) and the control class by 0.28 (low category). Based on the average test results using the independent sample t-test method, the understanding of physics concepts and the critical thinking skills of the experimental class was eminent to the control class. Furthermore, the research instruments were tests of understanding concepts and critical thinking skills with an average increase of 0.71 in the high category and 0.28 in the low category, respectively. The result showed that the implementation of guided inquiry learning models has the ability to significantly improve the understanding of static fluid concepts and critical thinking skills of Vocational High School students compared to conventional learning. This was because it provided the opportunity for students to independently construct a concept through problem presentation, formulation of hypotheses, data collection, analysis, and conclusions.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".