A Qualitative Analysis on the Occurrence of Learned Helplessness among EFL Students
Bibliographic record
Abstract
This study seeks to provide some comprehensive information on EFL students’ learned helplessness whenlearning English, by exploring the causal relationship between three variables: failure to learn English,personality traits and intrinsic/extrinsic motivation. Eighteen students and two English teachers participated inthis study with a gatekeeper’s consent. This was done mainly through focus group interviews. The data waselicited from the NEO-FI Personality Trait Inventory with a cross analysis done on the in-depth interviews. Thefindings indicate that students with different personality traits do regard their failure to learn English in differentways. This in turn, leads to various influences that decrease their intrinsic motivation. The intrinsic motivationfor all of the participants is inevitably affected by failure, but learners with a neuroticism trait are the mostsensitive to failure. Therefore, these are the students who are most susceptible to experiencing learnedhelplessness when learning English, while students with traits of openness, extraversion and agreeableness canreactivate their extrinsic motivation when appealed to by external incentives. However, this study alsodiscovered that there was a mismatch between a teacher’s judgment on student amotivation and the student’sself-assessment of his/her motivation to learn English.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".