The Effectiveness of Implicit and Explicit Instruction on German L2 Learners’ Pronunciation
Bibliographic record
Abstract
Abstract Previous research has investigated the effectiveness of implicit and explicit instructional methods on second language (L2) learners’ grammatical accuracy. However, there is a scarcity of studies focused on the effects of the two teaching methods on L2 learners’ pronunciation. To fill this gap, the present study examined the effects of implicit and explicit instruction on the pronunciation of beginning learners of German. Over the course of one semester, one group of learners (n=5) was taught pronunciation explicitly (i.e., using phonetic rules), another group (n=5) implicitly (i.e., without phonetic rules), and a third group (n=5) received no pronunciation instruction. A pretest‐posttest design was used to measure learners’ improve ment in accent and comprehensibility. A slight improvement in both variables was observed under all conditions, but no significant difference in progress was found across the three groups. The findings suggest that some learner variables (e.g., age) might be better predictors of improvement than the type of instruction. Moreover, not all pronunciation features were equally relevant for L2 learners’ comprehensibility and accent. The results have implications for L2 pronunciation 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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".