Prompting in CALL: A Longitudinal Study of Learner Uptake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it