ANALISIS KESULITAN SISWA DALAM MENYELESAIKAN SOAL TURUNAN FUNGSI ALJABAR DITINJAU DARI KECERDASAN EMOSIONAL
Bibliographic record
Abstract
This study aims to analyze and describe students difficulties in solving problems derived from algebraic functions in terms of emotional intelligence in class XI SMA Negeri 4 Central Maluku. This type of research is quantitative-qualitative research. The source of data in this study was students of class XI MIPA 3 SMA Negeri 4 Central Maluku with a total of 28 students, and the subjects in this study were 2 students including 1 student in the high emotional intelligence category, and 1 student representing the medium emotional intelligence category. The instruments used in this study were emotional intelligence questionnaires, algebraic function derivative test questions and interviews. The results showed that the difficulties experienced by students of class XI MIPA 3 SMA Negeri 4 Central Maluku in solving problems derived from algebraic functions include difficulties in understanding concepts, difficulties in understanding principles, and difficulties in operation. The results of the emotional intelligence questionnaire showed that the emotional intelligence of students of SMA Negeri 4 Central Maluku was in the high category. The results of the questionnaire averaged the percentage of emotional intelligence of students with high categories of 71.43%, emotional intelligence of students with moderate categories of 28.57%, and the average emotional intelligence of students with low categories of 0%, meaning that none of the students had low levels of emotional intelligence
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".