Task‐Related Variation in Computer‐Assisted Language Learning
Bibliographic record
Abstract
Abstract This study investigates task‐related variation in learner performance in a computer‐assisted language learning (CALL) environment. For our study, we collected data from 15 beginner and then intermediate second language (L2) learners of German who worked on 3 distinct activity types over 16 months: free composition, translation, and sentence building. Study results reveal that grammatical accuracy with respect to German word order was significantly higher with the meaning‐focused task type (i.e., free composition) for both the beginner and intermediate levels. Moreover, proficiency level also had a significant effect on L2 word order accuracy: Beginner students performed significantly better than intermediate learners on the two form‐focused task types (i.e., translation and sentence building). With the ultimate goal of understanding learner performance as it relates to different task types and success in CALL, this article provides possible explanations of these study results and suggests areas for future development of task design in CALL.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".