Effectiveness of Korean Classical Novels’ Usage in an English as a Foreign Language Classroom
Bibliographic record
Abstract
The current bilingual theories argue that the L2 language practices of bilingual students are derived from a single language repertoire and that enabling students to have access to their entire language repertoire can be an essential resource for further language development. Recent academic studies show that using students’ language repertoire, including L1 language in the target language classroom, increases their intrinsic motivation and improves fluency; however, many researchers still show skepticism regarding this teaching pedagogy. Therefore, this research validates that using L1 text improves students’ intrinsic motivation and fluency and increases curiosity about L2 culture while learning L2 language. In order to prove this, the study used three classical Korean novels written by Park Ji Won. The study included 98 students from intermediate and beginner speaking in an English as a Foreign Language (EFL) classes. A task-based assignment was given, and a questionnaire was used to ascertain the students’ opinions on the use of L1 text to improve motivation, fluency, and curiosity. This study reveals that EFL students can acquire a second language by learning about their traditions and culture, not just a practical and utilitarian text. In addition, this research proves that increasing curiosity and knowledge are integral components in language development, not only with the L1 text but also congruent with the L2 text, which is essential to acquiring the target language. The implication of this study emphasizes that not only L2 texts but also L1 texts are crucial for language development.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".