Raising the bar: language testing experience and second language motivation among South Korean young adolescents
Bibliographic record
Abstract
Drawing on second language (L2) motivation constructs modelled on Dörnyei’s (2009) L2 Motivational Self System, this study explores the relationship between language testing experience and the motivation to learn English among young adolescents (aged 12–15) in South Korea. A 40-item questionnaire was administered to middle-school students ( N = 341) enrolled in a private language school ( hakwan ). Exploratory factor analysis (EFA) identified five salient L2 motivation factors. These factors were compared to four learner-background characteristics: gender, grade level, L2 test-preparation time, and experience taking a high-stakes university-level language test. The results suggest that second language motivation, based on the L2 motivation factors identified as most salient in this educational context, was significantly associated with the amount of time spent preparing for language tests and experience taking a high-stakes language test intended primarily for university-entrance purposes. Young South Korean adolescent learners’ testing experiences and their motivation to learn English are discussed in relation to the social consequences of test use and ethical assessment practices.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".