Demotivating Factors for Saudi EFL Learners: A Comparative Study between Arts and Science Tracks Preparatory Year Programme Students
Bibliographic record
Abstract
While several studies in L2 deal with motivation in English language learning, demotivation has recently become the focus of much research. Following Sakai and Kikuchi's (2009) theoretical framework, this study aims to investigate the demotivation factors that affect Saudi students learning English in the Preparatory Year Programme (PYP) at a Saudi university. The data were collected from a random sample of 221 university students from the Arts Track (103) and the Science Track (118). Quantitative and qualitative data were collected using a questionnaire with two open-ended questions and analysed using SPSS and content analysis. The results revealed that the main demotivating factor that affects both tracks is ‘experience of failure and test score’. In addition, there was no significant difference between the students on the Arts Track and the Science Track pertinent to factors such as: 'experiences of failure and test scores', 'class environment', and 'learning content and materials.' However, there was a significant difference between the two tracks related to 'teachers' competence and teaching styles', 'characteristics of the classroom' and students' 'lack of interest' factors. The study's findings may have broader implications because they suggest that teachers can significantly impact students' intrinsic motivation as they can motivate low-performing students and those who have failed. It also suggests that teachers should dissuade students from comparing themselves to their classmates, avoid making comparisons among students, and use more communicative language teaching methods. Saudi pre-service and in-service EFL instructors should be better prepared to identify the primary sources of their students' demotivation and provide solutions to improve the learning environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".