Learning Cycle 5E Model and Junior High School Students’ Scientific Literacy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it