The Effects of Problem-Based Learning (PBL) Model, Educational Techniques, Creative Thinking Skills, Self-Confidence and Metacognitive Skills on Students' Biology Information Retention
Bibliographic record
Abstract
Student retention in science education remains a critical challenge, particularly in Biology, where conceptual complexity often leads to limited long-term understanding. This study aims to investigate how metacognitive skills mediate the relationship between instructional strategies and students’ retention in Biology, focusing on the effects of Problem-Based Learning (PBL), educational technology, creative thinking skills, and self-confidence. A quantitative, cross-sectional research design was used, involving 329 senior high school students in Gorontalo, Indonesia. Data were gathered through validated questionnaires and observation sheets, then analyzed using descriptive statistics and Structural Equation Modeling-Partial Least Squares (SEM-PLS). Descriptive results showed that the PBL model and educational technology were rated positively, while creative thinking, self-confidence, metacognitive skills, and retention were rated moderately high. SEM-PLS analysis revealed that PBL, educational technology, creative thinking, and self-confidence significantly influenced metacognitive skills. Creative thinking and self-confidence also had significant direct and indirect effects on retention. However, PBL and educational technology had no significant direct effects on retention, although their indirect effects through metacognitive skills were significant. Metacognitive skills play a pivotal mediating role in enhancing student retention in Biology. The findings underscore the need to integrate instructional methods and learner attributes that support metacognitive development, offering valuable insights for science educators and curriculum designers.
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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 | 0.013 |
| 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 | 0.002 | 0.001 |
| 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".