Development of a Science, Technology, Engineering, and Mathematics Curriculum for High School Students Using a Baseline Structure of Renewable Energy
Bibliographic record
Abstract
This work aims to study alternative energy using a learning and teaching curriculum to achieve outcomes for student projects. Development of a science, technology, engineering, and mathematics curriculum for high school students was done using a renewable energy baseline structure. The satisfaction of those who completed this curriculum is examined. Learners can use the opportunity to apply their knowledge and skills to further their academic careers at the university level or incorporate them into their daily lives, as renewable energy is of great importance to the nation and students. Twenty-five participants were selected using purposive random sampling. They were students and teachers of science, mathematics, and technology. The research involved analysis of the existing curricula and defining the objectives of sample groups. Content that aligns with the study objectives was developed, focusing on creating alternative energy projects. The designed curriculum is divided into four learning units, solar energy, wind energy, and hydropower, as well as a section on biomass and biogas. The curriculum was evaluated by ten experts and found highly suitable. Teaching effectiveness with a sample group of high school students revealed an effectiveness score of 80.10/83.00, which is higher than the established criterion, 80/80 (pre-test/post-test scores). Pre- and post-testing of the student group revealed that post-learning was significantly higher than the pre-learning results at a statistical significance of α=.05. Additionally, there was high satisfaction with the curriculum, consistent with the research hypothesis.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".