Intentional and Incidental Vocabulary Learning: The Role of Historical Linguistics in the Second Language Classroom
Bibliographic record
Abstract
Abstract Although English and German are both Germanic languages, due to various historical changes, many of their cognates are no longer easily recognizable. This study examined whether knowledge of language history can be beneficial to learners when learning English–German cognates. Thirty‐five English‐speaking second language (L2) learners of 3rd‐semester German at an American university were assigned to either an intentional or incidental learning condition. The intentional group received explicit instruction on 2 historical sounds shifts (Second Germanic Sound Shift, Ingvaeonic Palatalization) and relevant historical semantic changes. In contrast, the incidental group carried out various activities that exposed learners to cognates through reading and oral communication tasks. Results indicate that the intentional group significantly outperformed the incidental group, suggesting that knowledge of language history can be beneficial to learners when learning historically related languages. Declarative knowledge of the historical changes led to significantly greater vocabulary gains and it also provided learners with a tool kit to correctly predict the meaning of several cognates they had not previously encountered before. This study has broad implications for vocabulary learning, language teaching, and applied historical linguistics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".