The Effects of Implicit Learning on Japanese EFL Junior College Students’ Writing
Bibliographic record
Abstract
The current study aims to examine the effects of implicit learning on Japanese EFL junior college students' writing.The concept of written corrective feedback (WCF) has continued to receive much attention in second language acquisition research.Although most researchers have been supportive of explicit WCF for the development of accuracy, others have focused on implicit WCF with selfcorrection.While explicit instruction from teachers is the traditional method to provide students with corrective feedback, research in second language acquisition has shown growing interest in the role of implicit learning to improve students' writing skills.To investigate the impact of implicit learning on students' writing, 39 Japanese second-year students who have previously failed a compulsory writing class because of their high absenteeism, participated in this study.As treatments to improve writing skills through implicit learning, implicit tasks and self-correction were used to motivate the students.The design of the experiment includes two types of implicit tasks, implicit error correction and concept mapping during class.In addition, self-correction on homework was implemented.A mix of quantitative and qualitative methods was used in the analysis.The results showed that implicit learning appeared to help students in developing writing skills, but the impact may vary across students with different levels of English proficiency.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".