Differential Linguistic Development of Japanese Language Learners in Elementary School
Bibliographic record
Abstract
This article represents the fourth year of research on a project documenting and evaluating a core Japanese language program, referred to herein and in the United States as foreign language in elementary school (FLES). In this Year 4 article, we analyze data for a sample of 32 students, comparing their collective growth from Year 3 to Year 4. The data for the 32 sampled students reveals that overall linguistic growth was significant in Year 4. Next, we profile and provide a cross-case analysis of six students, taken from the sample of 32, who have participated in the JFLES program since its inception. By analyzing multiple data points for the six learners, three of whom were novice learners and three of whom were intermediate learners, we gain an in-depth view of pre-adolescent (fourth and fifth grade) FLES students who have participated in a well-articulated FLES program for four years. The profiles reveal differential linguistic development and differential attitude towards the JFLES program. Finally, we argue that existing second language assessment practices that label young language learners as high and low achievers may be problematic. Our research demonstrates the importance of employing multiple measures when assessing the language learning of children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".