EFL Saudi Students’ Class Emotions and Their Contributions to Their English Achievement at Taif University
Bibliographic record
Abstract
This paper investigates class academic emotions of anger, anxiety, enjoyment, hope, hopelessness, pride, boredom and shame in the academic situation and their contributions to Saudi EFL students’ English achievement. It also strives to find if there are any differences in class emotions and achievement according gender and streams (science and humanities). The sample consists of 315 (177 males & 138 females) university students. The Pekrun, Goetz, Titz and Perry (2002) class-related emotions scale was used to determine class emotions of students. The findings revealed that there are no differences between science and humanities (M = 22.763, SD = 8.118) in achievement and class emotions except in boredom in favour of science stream. The results also indicated that there were no differences between males and females in class emotions except in the enjoyment in favour of females. It also revealed that there were significant differences between males and females in English achievement in favour of females. On the other hand, it was found that emotions explained 65.8 % in variance of the students’ academic achievement. Furthermore, the findings have implications for students, teachers, and curriculum developers who are to develop a curriculum well-suited with the needs of language learners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".