Bibliographic record
Abstract
This research presents a longitudinal study of learner uptake in a computer‐assisted language learning (CALL) environment. Over the course of 3 semesters, 10 second language learners of German at a Canadian university used an online, parser‐based CALL program that, for the purpose of this research, provided 2 different types of feedback of varying degrees of specificity: Metalinguistic explanations (ME) and metalinguistic clues (MC). Results indicate that feedback specificity affects learner uptake in different ways. Cross‐sectionally, the study reveals significant differences in learner uptake for the 2 more advanced courses, German 103 and 201, whereas for the introductory course, German 102, no significant difference for the 2 feedback types and their effect on learner uptake was found. Results of the longitudinal data indicate that there is a significant increase in learner uptake from German 102 to 201 for the error‐specific feedback (ME), whereas learner uptake for the generic feedback type (MC) varies insignificantly across the 3 courses. Finally, the study shows a significant impact of the 2 feedback types on learner uptake independent of error type (grammar and spelling).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".