Improving Flunked English Majors Performance through Enhancing Students' Perception of Self-Efficacy and Outcome Expectancy at Shaqra University
Bibliographic record
Abstract
Teachers have to support flunked English majors emotionally, academically and use creative methods to improve their academic performance. The present research examined the effect of self-efficacy and outcomes expectancy on improving the performance of flunking English majors at Shaqra’ University. The instruments required for the research were a follow-up interview, pre-posttests and a questionnaire of self-efficacy and outcomes expectancy. The sample size was thirty of flunking English major students. The research compared eight techniques to enhance self-efficacy and outcomes expectancy to regular method. To test the hypotheses, data were collected and statistically analyzed. The results showed that there was no significance difference between the control group and experimental related to regular method in the pre-tests, whereas there was a difference in the post-tests in favor of the experimental group attributed to the treatment. The findings of the questionnaire proved that students' perception of self-efficacy and outcome expectancy were crucial to enhance students' academic performance. The research suggested further investigations to examine the effect of self-efficacy and outcome expectancy on enhancing language oral and written skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".