Promises and Obstacles of L1 Use in Language Classrooms: A State-of-the-Art Review
Bibliographic record
Abstract
Translation and language teaching techniques which take language learners’ first language (L1) as point of reference for teaching the second language (L2) have been long discouraged on the ground that these teaching techniques would end in the fossilization of L2 structure forms in the learner’s Interlanguage system. However, in recent years, the status of L1 use in L2 teaching and learning has revived as a result of the recognition that L1 can serve purposes in L2 teaching and learning (Hunt, 2012). In the last two decades, strong theoretical arguments have been posed for L1 use in language classrooms. Most of these arguments are based on the ground that L1 use can facilitate the processes of both L2 teaching and acquisition. Abundant research has been done in recent years to validate these theoretical arguments. The current paper would give a review of this research, with reference to three L1-based techniques for language teaching and learning that have appealed most to L2 researchers (i.e., translation, code switching, and L1 glossing). The conclusion drawn from this research is that language learners can benefit L1 use and L1-based techniques in their L2 acquisition. Further, along with the theoretical arguments and empirical evidence in defense of L1 use in language classrooms, L2 learners and teachers have begun to express more positive attitudes towards L1 use, and related techniques, in their own classrooms. Yet, there remain some challenges and obstacles for L1 use in language use. Suggestions are made as to how to address these challenges so that L2 pedagogy and use would benefit most from L1 use in language classrooms.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".