Comparing Instructional Methods for Address Pronouns in Second Language German
Bibliographic record
Abstract
The German language utilizes three address pronouns to express the second-person pronoun ‘you’; du is the singular informal pronoun, Sie is the singular and plural formal pronoun, and ihr is the plural informal pronoun. As a result of social movements, the German address system has changed and developed over time, and there are now multiple perspectives about what the default singular address form should be (i.e., du or Sie) in new interactions. These competing systems can pose problems even for German native speakers (NSs), as they navigate social situations. Previous research investigating address among second language (L2) learners has shown consistently that without direct instruction, learners have poor control over their address choice. Within classroom instruction, time is already limited, and textbooks examples can be oversimplified or lack contextualization; thus, a new approach is needed to instruct learners. The present study compares implicit and explicit instruction in a computer-assisted language learning environment (CALL) on the effect of address choice among second language (L2) German learners. To accomplish this, address behaviour data were gathered from NSs in Hamburg, Germany and from L2 learners in Calgary. The NS data served a baseline from which to measure pragmatic development of L2 learners, and they also informed the instruction of the implicit and explicit training modules delivered to the learners. A pre-test, immediate post-test and delayed post-test were used to measure improvements towards native-like address behaviour. Results show that L2 learners exposed to explicit instruction had immediate and sustained pragmatic development, and little pragmatic development was observed for participants instructed implicitly.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".