Investigating EFL Achievement Through the Lens of Demotivation
Bibliographic record
Abstract
It is generally agreed that demotivation is any forces that reduce a student’s passion or enthusiasm to learn. Despite some studies on demotivation across language levels, culture, and languages; research has not been conducted on university students from different language levels in Thailand. The aim of this mixed-method study is three-fold. Firstly, it attempts to factorize types of demotivation among English major students. Secondly, it examines the effect of demotivation on EFL achievement. Thirdly, it investigates the differences of demotivation in different levels of EFL achievement. The study involved undergraduate students majoring in English completing a questionnaire and being interviewed. The results revealed that there were four potential types of demotivation among English major students: 1) Media, teaching styles and teacher competence; 2) Attitudes towards teachers and classmates; 3) Experiences of failure and attitudes towards English learning, and 4) Characteristics of lessons and class materials. Findings also showed that Demotivation Type 3 (Experiences of failure and attitudes towards English learning) influenced EFL achievement (p <.001). Interestingly, there was a significant difference in the degree of influence of Type 3 among low, moderate, and high levels of EFL achievement. In the conclusion, pedagogical implications of these findings are discussed in order to help teachers understand important factors that demotivate students to achieve in English language learning so that the occurrence of those factors may be avoided.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| 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".