Designing a Knowledge Representation Approach for the Generation of Pedagogical Interventions by MTTs
Bibliographic record
Abstract
Model-tracing tutors (MTTs) have proven effective for the tutoring of well-defined tasks, but the pedagogical interventions they produce are limited and usually require the inclusion of pedagogical content, such as text message templates, in the model of the task. The capability to generate pedagogical content would be beneficial to MTT frameworks, as it would lessen the task-specific efforts and could lead to the capability of providing more sophisticated pedagogical interventions. In this paper, we show how Astus, as an MTT framework, strive to attain a higher level of automation when generating pedagogical interventions compared to other MTT frameworks such as TDK and CTAT’s MTTs. This is achieved by designing a knowledge representation approach in which each type of knowledge unit has a clearly defined semantic on which the MTT’s pedagogical module can rely on. We explain how this knowledge representation approach is implemented as a knowledge-based system in ASTUS and show how it allows the development of MTTs that can automatically generate the pedagogical content required to provide next-step hints and negative feedback on errors. Multiple small-scale experiments were conducted with computer science undergraduate students in order to obtain a preliminary assessment of the effectiveness of Astus’s pedagogical interventions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".