Encouraging Students’ Critical Thinking Using Problem-Based Book Integrated Daily Problems and Solutions about Environmental Pollution
Bibliographic record
Abstract
Using learning books for students related to everyday problems and their solutions is important to support the empowerment of students' thinking. This research aims to encourage students' critical thinking (CT) using a problem-based book integrated with daily problems related to environmental pollution. Quasi-experimental-based research was used to investigate the CT of 103 junior high school students. Forty-nine students were given an intervention using a problem-based book integrated with daily problems about environmental pollution. A total of 54 students were not given intervention (control class). Students' CT was collected using tests validated by experts and empirically and met reliability. CT n-gain was calculated based on pre- and post-CT values. Students' CT priors show differences, so the calculation of the intervention effect uses n-gain data. The t-test calculates CT differences in the intervention and nonintervention classes. Data analysis shows that using a problem-based book integrated with daily problems about environmental pollution affects increasing students' CT, which is significant (Sig. n-gain < 0.05). Students in the experimental class showed a moderate increase, while students in the control class showed a low CT increase. Presenting daily problems related to the environment around students has increased critical discussion during learning. This condition causes CT students to be encouraged to be better during learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".