Intelligent Tutoring Systems: Confluence of Information Science and Cognitive Science
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
The advent of Internet as a global communication medium has brought a new focus on an area of research in designing Intelligent Tutoring System (ITS) that has not been adequately considered so far. In the main, this has been due to the localised nature of most academic environments limiting the sources of information and an implicit assumption that information and knowledge are synonymous. These factors have led to overemphasis on learner modelling in the traditional ITS research, which seeks to enhance the interaction between the ITS as the provider and the learner as the consumer of knowledge, ignoring the crucial role played by the teacher in enhancing the learning in a given context. The limitations of the traditional approach become more visible when educational information is sought to be transmitted across long distances and the need for adaptation to local contexts becomes apparent. This paper argues that the human teacher, as the manager of learning, plays a vital role within the joint cognitive system consisting of the teacher, ITS, learner and learning peers. This role needs to be recognised by ITS designers by through a teacher model. It also suggests that ITS may perhaps best embody the emerging framework of Informing Science.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.016 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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