Problem Solving Skills and Learning Achievements through Problem-Based Module in teaching and learning Biology in High School
Bibliographic record
Abstract
The purpose of this study was to examine the effects of using problem-based module (PBM) in the subject of Biology on high school students’ problem-solving skill and achievement. This research used the quasi-experiment method with Non-Equivalent Pretest and Posttest Control Group Design, which involved two science classes, in which one group was assigned as control group and another one as experiment group, in a high school in Pekan Baru, Indonesia. The problem-solving ability and the product of learning were descriptively analyzed before being inferentially analyzed. To find out whether or not there is any difference in their problem-solving skill, t-Test and N-gain test was conducted on the experimental group’s and control group’s concept mastery level and product of learning. The result shows that the problem-solving skill percentage of the experimental group was 95.47% (very good), whereas that of the control group was 25.12% (low). The average of student’s achievement in the experimental group was 84.26% (good), while that of the control group equaled 79.08% (moderate). The average of the product of learning was 89.89% (good) for the experimental group, whereas that of the control group was 52.10% (low). The findings showed that PBM can actually increase problem-solving skill, students’ achievement, and students’ learning product, with the experimental group getting higher percentage in all three aspects compared to the control group by using PBM in their Biology class. The implication of this study is the increase in the quality of learning through learning innovation using learning module. The panned and organized implementation of this module by teachers will not only improve students’ thinking skills, but also increase the quality of science and technology, consistent with the aim of Indonesia 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".