Enhancing Metalinguistic Awareness Through Microlearning: A Comparative Analysis of Catalan Acquisition Between Multilingual French and English Learners
Bibliographic record
Abstract
Knowing that previously learned languages may influence and support language learning among multilingual learners, equipping educators with diverse tools and resources empowers them to make more informed decisions in facilitating language learning. This holds particular significance in the case of minority languages like Catalan, where resource availability tends to be more restricted. This paper presents the findings of a project aimed at identifying the specific needs of multilingual B1 learners of Catalan. Using a comprehensive five-way classification system, the study compares the written productions of multilingual learners from different linguistic backgrounds (English and French L1 learners of Catalan). Analyses revealed a similar pattern regarding the nature of the errors, with incorrect word selections at the lexical-semantic, syntactic, and orthographic levels being the most frequent. Both groups differ significantly in terms of the distribution of errors; however, they did not differ when the analyses were conducted by error types. In both cases, the most frequent error was the lexical-semantic misselections, with false analogies and incomplete applications of rules explaining the errors. These findings led to the creation of just-in-time microlearning capsules tailored to address these issues. The microlearning capsules were designed to enhance students' metalinguistic awareness. Students' and teachers' perceptions of the tool were also assessed, and both groups predominantly viewed interlinguistic information as beneficial to language learning. This project highlights how microlearning, tailored to frequent errors, can bolster multilingual learners' acquisition and provides promising tools for language teaching.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".