Bibliographic record
Abstract
This chapter discusses the cyclical process of collecting and recycling learner data within the E-Tutor CALL system and presents a study on student usage of its data-driven learning (DDL) tool. E-Tutor consists of a static and dynamic learner corpus for L2 learners of German. The static learner corpus has been constructed from approximately 5000 learners who used the system over a period of five years. These learners provided millions of submissions from a variety of activity types. In addition, all concurrent E-Tutor users contribute data to a dynamic corpus, which allows them to compare and examine their ongoing system submissions to those contained in the static corpus. The authors conducted a study with 84 learners and recorded their interaction with the DDL tool of E-Tutor over one semester. Study results on student usage suggest that investigating sample input of a large, unknown user group might be less informative and of less interest to language learners than their own data. For the DDL tool to be useful for all proficiency levels, training and scaffolding must also be provided.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".