Comparison of State and Private Elementary School Students’ Motivational Attitudes Towards the English Course-Adana Province Example in Turkey
Bibliographic record
Abstract
The role of motivation in foreign language teaching has been a debated issue for long years. One of the problematic issues is the motivation problem encountered in foreign language courses. Because, the most important factor affecting academic success is motivation. Most researchers argued that as the motivation level increases, the level of foreign language learning will increase and students will learn foreign languages more easily. In this research, motivation attitudes of state and private elementary school students in foreign language courses in Turkey were examined. At the same time, the relationship between foreign language and motivation attitude of state and private elementary school students was tried to be determined. The sample of the research is 747 students in 5th, 6th, 7th and 8th grades selected by random sampling from a Private Elementary School and a State Elementary School in Adana Province, Turkey, in 2018-2019 academic year. In the research, the participants were applied the Academic Motivation Scale-AMS and there was a significant effect on the motivation of different school type (state and private), gender, school and primary school was not examined. The difference between motivation levels of the students in state and private schools was tried to be determined. In the study, Pearson Product Moment Correlation Coefficiency and Independent Sample t-test Analysis were performed by using Statistical Package for Social Sciences—SPSS 22 Programme. According to the results, it was found that the motivation level differed according to the gender and school type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".