Constructing a Data-Driven Learning Tool with Recycled Learner Data
Bibliographic record
Abstract
This paper discusses a data-driven learning (DDL) tool, which consists of a learner corpus for L2 learners of German. The learner corpus, in addition to submissions from ongoing current users, has been constructed from millions of submissions from a variety of activity types of approximately 5000 learners who used the E-Tutor CALL system over a period of five years. By following a cyclical process of development, implementation, and evaluation, adapted from the ADDIE model, E-Tutor helped us not only to inform language teaching pedagogy and to provide system enhancements generated by the outcomes of vast data collections, but also to expand an existing learning environment (e.g., Tutorial CALL) to include DDL. The article discusses the cyclical process of collecting and recycling learner data by also focusing on the design features of the DDL tool of E-Tutor within the ADDIE framework and providing data on student usage.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".