Kelayakan bahan ajar interaktif berbasis problem-based learning pembelajaran PPKn pada Siswa Kelas V SD
Bibliographic record
Abstract
The purpose of this study was to determine the feasibility of interactive teaching materials based on problem-based learning in Civics learning for fifth grade elementary school students in Mayong District, Jepara Regency. Sources of data with interviews, questionnaires, and documentation. Quantitative and qualitative research methods. Data were analyzed by accumulating the number of scores. Analysis of the data from the expert validation test results, student and teacher responses were obtained using the percentage calculation of the score obtained with the maximum score and description. The results of the research are the feasibility of teaching materials which are validated by material experts, teaching materials experts and practitioners each 90.00; 89.5, and 89 “very feasible” criteria. The results of the responses from students were 87.53% and the average response of the three teachers was 88.89% with the "very feasible" category. Problem-based learning-based interactive teaching materials are proven to be suitable for use as a teaching chart for Civics learning for fifth graders in elementary schools in Mayong District, Jepara Regency.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".