Effects of L1 Use on L2 Text Quality: Rethinking Cognitive Process of Formulating L1 Texts during L2 Writing
Bibliographic record
Abstract
The first language (L1) use is vital to developing the quality of second-language (L2) writings. Establishing a clear argument with the logical flow in L2 can be a daunting task for learners with low L2 proficiency. To determine if L1 use is positively related to students’ L2 texts, the researcher conducted a comparative study with 77 Japanese L2 learners. It examines differences amongst L2 argumentative essays resulting from four writing processes. The participants were divided into two focused groups, the experimental group formulating L1 texts and then translating into L2, and the contrast group composing texts directly in L2. Then, each group was divided into two sub-groups: One composing their texts using a writing framework, and the other with no framework. Over three L2 classes, each group experienced the writing process respectively. They submitted the essays before and after the processes. Two experienced L2 instructors assessed students’ pre- and post-texts, and compared the texts of each group cross-sectionally and longitudinally. The results show that participants in the experimental group with the framework significantly improved their L2 text quality. Thus, the teaching of argumentative writing should incorporate the process of L1 formulation with a framework into a process-focused approach to efficiently facilitate students’ L2 writing.
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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 | 0.067 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".