The Influence of Teacher’s Teaching Methods on Students’ Learning Enthusiasm
Bibliographic record
Abstract
This research aimed at finding out whether the teacher’s teaching methods influence learning enthusiasm of the students of SMAN 8 Makassar. This research employed a QUAN-Qual model. The population of this research involved 350 students from the eleventh grade. The participants of this research were 27 students of class XI IPS 2 and an English teacher. This class was taken as the sample through random sampling technique from 10 classes. The data were collected through the observation checklist, questionnaire, and interview. The findings of this research revealed that the English teacher’s teaching method of SMAN 8 Makassar Selatan influenced the students’ learning enthusiasm. It was proven by the result of the observation checklist, questionnaire, and interview. The observation checklist presented the teacher’s teaching method during the classroom which missed some indicators. The observation checklist for the students indicated less enthusiasm strengthened by the questionnaire revealing that most students were in moderate state of enthusiasm or in other words they have low degree of learning enthusiasm. Meanwhile, the result of the interview strengthened the data from observation checklist and questionnaire that the English teacher’s teaching method contributed to the students’ learning enthusiasm. Thus, the teacher’s teaching method is a determining factor of the students’ enthusiasm in learning especially learning English.
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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 | 0.003 | 0.014 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".