The Effect Of Self Confidence On The Ability Of Understanding Mathematical Concepts Of Junior High School Students On The Triangle And Quarter Matter
Bibliographic record
Abstract
This research aims to analyze and study in depth about the self-confidence-influenced skills of matematiS from middle school students. The methods in this study use correlational methods with quantitative approaches. The population in this study was junior high school students in west Bandung regency and the sample of 30 people was determined by purposive sampling technique at one of the junior high schools in West Bandung Regency. Instruments in this ability in the form of comprehension ability tests as many as 5 points of questions and self-confidence scale students as many as 24 statement scales. The results of this study concluded that there is a positive influence between self-confidence and the mathematical understanding ability of middle school students. This shows that the higher the student's confidence, the higher the student's mathematical comprehension ability. Factors that affect high self-confidence include: (1) students with a high confidence attitude do not hesitate in making decisions in solving problems (2) students can have many ideas in working on the problem at hand. Meanwhile, students with less confidence will tend to have difficulty in answering potluck questions, students only memorize not yet to understand the understanding of the concept so that the student does not dare to make decisions when solving existing problems.Kata kunci: self confidence, mathematical understanding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".