Description of Student Learning Outcomes between the Students who Follow Guidance Learning Outside the School with The Students are not Follow it in Biology Topics Class XI Science SMAN 7 Padang
Bibliographic record
Abstract
Factors influencing the success of a learning can come from both internal and external. Learners tend to have concerns in implementing learning to achieve good learning outcomes, so learners want to increase learning outside the school. Today, there are a lot of learning counseling institutions that are developing in various regions in Indonesia. This leads to a lot of interest of learners to follow the guidance of learning outside school. The purpose of this study is to see the contribution of tutoring outside school to the learning outcomes of learners who follow the guidance of learning outside the school on the subjects of biology class XI Science SMAN 7 Padang. The population of this research is all students of class XI IPA SMAN 7 Padang. Research samples are learners who follow and learners who do not follow the guidance of biological learning outside the school. The sampling technique used saturation sampling technique. The data analysis technique used is the median test, because one of the data is not normally distributed and the two groups are homogeneous. The average learning outcomes of learners that is 86.91, not too significant difference with those who do not follow the guidance of learning outside the school with an average of 84.88. The result of data analysis shows that there is no contribution of learning guidance to learners' learning outcomes which follow the guidance of study outside school with comparison of X2 count 0,270 and X2 table 3,84, so X2 count
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".