PENERAPAN MODEL PEMBELAJARAN CORE (CONNECTING, ORGANIZING, REFLECTING AND EXTENDING) UNTUK MENINGKATKAN PRESTASI BELAJAR PESERTA DIDIK PADA POKOK BAHASAN KESETIMBANGAN KELARUTAN (KSP) DI KELAS XI IPA SMAN 4 PEKANBARU
Bibliographic record
Abstract
The research about the CORE (Connecting, Organizing, Reflecting and Extending) model has been conducted in SMAN 4 Pekanbaru. The purpose of this research is to detemine wheterthe application of CORE learning can improve achievement and the influence of application of CORE learning model in class XI IPA SMAN 4 Pekanbaru on the subject solubility aquilibrium (Ksp). The type of this research is experimental research with experiment design randomized control group pretest-posttest. Sample of the research consisted of two classes, a class XI IPA 5 as an experimental class (implemented the CORE learning model) and class XI IPA 1 as the control class (without the CORE learning model). Test tecniques used as a tecnique in collecting research dats. T-test and coefficient determinasi was used as analysis technique. Based on the data analysis obtained t > t table is 5,71 > 1,66, meaning that the application of the CORE learning model can improve the student achievement on the subject solubility aquilibrium (Ksp) in class XI IPA SMA Negeri 4 Pekanbaru with the great influence of implementation of the CORE learning model on improving learning achievement is 31,178%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".