Variability in second language learning:the roles of individual differences, learning conditions, and linguistic complexity
Bibliographic record
Abstract
Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This paper examines the relationship between these three variables in language learners. Native English speakers were exposed to an artificial language containing three sentence patterns of varying linguistic complexity. They were randomly assigned to two groups – incidental and instructed – designed to promote the acquisition of implicit and explicit knowledge, respectively. Learning was assessed with a grammaticality judgment task, while subjective measures of awareness were used to measure whether exposure had resulted in implicit or explicit knowledge. Participants also completed cognitive tests. Awareness measures demonstrated that learners in the incidental group relied more on implicit knowledge, whereas learners in the instructed group relied more on explicit knowledge. Overall, exposure condition was the most significant predictor of performance on the grammaticality judgment task, with learners in the instructed group outperforming those in the incidental group. Performance on a procedural learning task accounted for additional variance. When outcomes were analysed according to linguistic complexity, exposure condition was the most significant predictor for two syntactic patterns, but it was not a predictor for the most complex sentence group; instead, procedural learning ability was.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".