Turkish High School Students’ English Demotivation and Their Seeking for Remotivation: A Mixed Method Research
Bibliographic record
Abstract
Since Gardner introduced the importance of motivation on Language 2 learning, the concept has been accompanied with three more relevant concepts; amotivation, demotivation and remotivation. This paper mainly focused on high school students’ de-motivation and remotivation in English. De-motivation is a set of factors which decreases the motivation level of the learners and re-motivation is an attempt to overcome those de-motivating factors. English learning-teaching process has been a problematic issue for a long time in Turkey. While there are researches focusing on the de-motivating factors in many countries, such a research for Turkish high school students has not been found. This research aimed to fill this research gap and to determine the English demotivation level of the students and the demotivating factors for them and to put forth suggestions to re-motivate the learners. An explanatory design was used as a mixed method research design. The sample was constituted of 579 students. Research results revealed that demotivation level of high school students in English is quite high, their motivation decreases most in high school period. Lack of interest in English, attitude of course teacher, classroom environment and course materials are among demotivating factors. In addition, the students request that, for remotivation, courses should be entertaining, technological tools should be utilized more and frequency of speaking activities should be increased.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".