Extending Moodle Functionalities to Adaptive Testing Framework
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
E-learning has advanced considerably in the last decades allowing the interoperability of different systems and different kinds of adaptation to the student profile or the learning objectives. But, some of its aspects, such as E-testing are still in their early age. As a consequence, most of the actual E-learning platforms offer only basic E-testing functionalities. In addition, in most those platforms, the tests are in the traditional format despite their known limitations and precision problems. However, by making efficient use of well known techniques in artificial intelligence, existing psychometric theories and standards in E-learning, it could be possible to integrate adaptive and more informative E-testing functionalities in the actual E-learning platforms. In this paper, we will present some of the principles, the architectural elements and the algorithms used in an exploratory integration of adaptive testing functionalities within the Moodle platform.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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