A Learner Model for Learning-by-Example Context
Bibliographic record
Abstract
Nowadays learning environments put more and more accent on the intelligence of the system. The intelligence of a learning environment is largely attributed to its ability of adapting to a specific learner during the learning process. The adaptation depends on individual learner's knowledge of the subject to be learned, and other relevant characteristics of the learner. The knowledge and the relevant information about the learner are maintained in the learner model. A learner model can be defined as structured information about the learning process; and this structure contains some values of the learner's characteristics. This paper proposes a new learner model, which is based on the consideration of what is appropriate to the learning-by-example context. The model records five categories of information about the learner: personal data, learner's characteristics, learning state, learner's interactions with the system, and learner's knowledge. This model is being integrated in Sphinx, an educational environment based on learning by means of examples.
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How this classification was reachedexpand
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 | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".