Constructing a Data-Driven Learning Tool with Recycled Learner Data
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 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.
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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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