Affective variables, parental involvement and competence among South Korean high school learners of English
Bibliographic record
Abstract
This study investigated the relationships between various affective variables and two measures of competence in English, for 190 South Korean high school students. A 55-item questionnaire was used to measure attitudes (Attitudes toward English Speakers and their Communities and Attitudes toward the English-speaking Culture), motivation (Motivational Intensity, Desire to Learn and Attitudes toward the Learning of English), amotivation, parental involvement (Active Parental Encouragement, Passive Parental Encouragement and Parental Pressure), parental disinterest and students’ competence in L2 (English- EXAM and English-SELF). Pearson product-moment coefficients indicate that active and passive forms of parental encouragement correlate with motivationto learn, as conceptualized by Gardner (1985, 2010), as well as with parental pressure, which suggests that South Korean students report undergoing forms of pressure when their parents actively or passively encourage them. Furthermore, the obtained correlations of the active and passive forms of encouragement with different variables suggest that the two forms represent two distinct concepts. While parental disinterest correlated negatively with motivational variables, parental pressure correlated only with motivational intensity, and only weakly. Therefore, parental pressure seems not to interact significantly with participants’ attitudes, motivation and competence. Multiple linear regression analyses confirm the importance of motivation to learn for students' L2 competence.
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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 | 0.000 | 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) | 0.001 | 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".