Intelligent Tutoring of Domain Skills : The Need and A Solution
Bibliographic record
Abstract
The task-oriented disciplines require acquisition ofphysical and cognitive skills, besides the domain’s conceptual knowledge to get ready for challenges of real work environment. Traditional academic practices tend to emphasize facts acquisition and fail to provide adequate learning of cognitive skills required in the day-to-day application of these facts in real life, requiring the learners to subsequently acquire these through experiential learning at the work place and thus delaying the productive use of domain knowledge. On the other hand, learning only in the real work environment makes the learner competence too situated to the particular context in which learning takes place and the learners frequently lack the ability to generalize or even distinguish between the concrete and abstract aspects their knowledge, reducing the scope of immediate productive use of their competence in different situations that may not be completely identical to their place of learning. This paper describes an intelligent tutoring system, developed under the Byzantium project, which attempts to bridge this gap and aims to facilitate acquisition of cognitive skills to go with the learning of a domain’s concepts
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".