Relationship Between Introversion/Extroversion Personality Trait and Proficiency in ESL Writing Skills
Bibliographic record
Abstract
The study aims at investigating the role of extroversion/introversion personality traits in learning writing skills of English as a second language. The selected sample for the research consisted of 57 participants who undertook instruction on ‘Essay Writing and Presentation’ for six months as a formal course of study in their MA English Program. The research tools consisted of a questionnaire and an achievement test on writing skills. The questionnaire consisted of 30 items all adopted from Eysenck’s Personality Questionnaire to measure the introversion/extroversion traits of students’ personality. After identifying their personality trait (i.e., introvert, extrovert and neurotic), the participants were given an achievement test on writing skills. The participants’ scores in the achievement test were submitted to SPSS and independent sample t-test was applied. The findings reveal that a significant difference exists between the writing achievement of introvert and extrovert learner groups. However, no difference is found between the writing performance of neurotic and introvert learner groups or between neurotic and extrovert learner groups. The results also reveal that introverts are better learners of ESL writing skills as compared to the extroverts.
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 | 0.000 | 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 | 0.000 | 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".