English Language Learning Demotivating Factors for Saudi High School EFL Students
Bibliographic record
Abstract
Demotivating factors are one of the sources that can reduce students’ motivation toward language learning. This study investigated language learning demotivating factors among high school EFL students. It also explored the educational implications and recommendations for promoting EFL students’ motivation from teachers’ perspectives. A total of 365 Saudi high school EFL students and 18 secondary English language teachers from six public schools participated in the study. The data of the study were collected via two research instruments: a questionnaire and semi-structured interviews with students and teachers. The results revealed that subject- related and teacher-related demotivating factors were the most reported demotivating factors for Saudi high school EFL students. The results also showed that lack of interesting topics, lack of activities for practicing English, overemphasis on grammar, and incompetence of teachers were the most demotivating factors for EFL students toward English learning. Moreover, several recommendations for promoting students’ motivation have been suggested by teachers such as technology use, extrinsic motivation and encouragement, and competitive and collaborative work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".