A Fully Personalized Adaptive and Intelligent Educational Hypermedia System for Individual Mathematics Teaching-Learning
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
In this study, architecture and development process of UZWEBMAT, an adaptive and individualized environment based on learning styles and supported with expert system, are presented. UZWEBMAT was developed to teach probability unit of 11th grade mathematics course. Three different contents, which are appropriate to Visual-AuditoryKinesthetic (VAK) learning styles, are presented to learners. Content of each sub-learning style is presented with expert system support. Thanks to this expert system, it is possible for learners with same learning style to take different contents. By this means, highest level of individualized learning environment was tried to be created via UZWEBMAT. Integration of UZWEBMAT to real classroom environment shall be made in future studies. Evaluation of UZWEBMAT and its impact on academic achievements of learners shall be researched.
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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 | 0.000 |
| 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