Learning Cycle 5E Model and Junior High School Students’ Scientific Literacy
Bibliographic record
Abstract
General Background: Scientific literacy is a crucial 21st-century skill that enables students to apply scientific knowledge in solving real-life problems. Specific Background: In Indonesia, the results of PISA consistently show low levels of scientific literacy among students, highlighting the need for effective instructional models. Knowledge Gap: Previous studies have explored the Learning Cycle 5E model but rarely examined its role in addressing scientific literacy using the Pan-Canadian Assessment Program (PCAP) indicators. Aim: This study aimed to investigate the Learning Cycle 5E model in improving scientific literacy among junior high school students. Results: Using a quantitative pre-experimental design with a one-group pretest-posttest, findings revealed an N-gain of 0.6 (moderate category) and no significant differences across classes based on ANOVA (p = 0.126). Analysis of indicators showed improvements with scientific inquiry (70%, good), problem-solving (56%, fairly good), and scientific reasoning (43%, fairly good). Novelty: The study highlights the integration of PCAP indicators into the Learning Cycle 5E framework, providing a structured evaluation of scientific literacy beyond content mastery. Implications: These findings suggest that the 5E model supports active and inquiry-based learning, which can be adapted by teachers to foster better scientific literacy outcomes in junior high schools. Highlights : Structured evaluation of scientific literacy using PCAP indicators Improvement in scientific inquiry, problem solving, and reasoning Practical guidance for teachers in junior high school science Keywords: Learning Cycle 5E, Scientific Literacy, PCAP Indicators, Junior High School, Science 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".