Application of Contextual Teaching and Learning Approach on Statistics Material Against Student Results
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
Based on empirical data, student learning outcomes on statistical material are not in accordance with the expectations of teachers so it is necessary to apply the Contextual Teaching and Learning (CTL) approach. A CTL implementation can help students to understand concepts and skilled resolve issues. The research purpose is to see whether the students learning outcomes on statistics materials that are taught using the CLT approach are better than conventional approaches. The methods used are experimental studies and the data will be obtained from the pre-test and post-test learning outcomes. The population are the entire class IX student while samples taken by random sampling techniques, two classes, namely as an experimental class and control. Research design is a Non-equivalent Control Group Design. Based on the results with two average difference tests on T-Test statistics were obtained that student learning outcomes in experimental class is higher than the controls. This means learning by applying the CTL approach can improve learning outcomes and the benefits for mathematics teachers gained insight into the CTL learning process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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