Pengaruh Metode Pembelajaran Mind Mapping terhadap Hasil Belajar Siswa SMP Kristen Ta’Aba pada Mata Pelajaran IPS
Bibliographic record
Abstract
Social studies learning as one of the progmatic components in the school curriculum is actually expected to support the achievement of the ideal goals of education. Through social studies education, students are fostered and developed their intellectual mental abilities to become citizens who are skilled and socially concerned and responsible in accordance with the values contained in Pancasila. Social studies learning contained in the educational curriculum is a simplification of learning materials from social science knowledge or concepts. The method in this study is research using a quantitative research approach. This research is descriptive. The sample in this study was 30 students of grade VIIIA (control) and VIIIB (experiment) of SMP Kristen Taaba District. Data collection techniques using test questions and documentation. Based on the results of research and discussion, and data analysis carried out, it can be concluded that the mind mapping learning model can affect the learning outcomes of social studies class VIII SMP Kristen Taaba District. Indicators of the assessment of learning outcomes can be seen from the students' posttest scores have increased. Based on the research objectives with the t-test hypothesis, an sg (2 – Tailed) value of < 0.05 is obtained which is 0.000 so that H0 is rejected meaning that there is an influence of the mind mapping learning model on the learning outcomes of social studies class VIII SMP Kristen Taaba District. In other words, the mind mapping learning model affects learning outcomes with categories that are quite effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".