Transformer-Based Semantic SBERT Robot with CI Mechanism for Students and Machine Co-Learning
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Bibliographic record
Abstract
This paper proposes a transformer-based semantic robot with a computational intelligence (CI) mechanism designed for use in an educational co-learning environment, where teachers, teaching assistants, and students interact with the CI robot and attention ontology to enhance the learning process. The approach is applied in two distinct applications. The first, focusing on student-machine co-learning with writing performance evaluation, involves an attention-based mechanism for curating learning content from students, which is further refined by a preprocessing mechanism with expert-based fuzzy numbers. The second, concentrating on student-machine co-learning with speaking performance evaluation, introduces a Meta AI Universal Speech Translator (UST) Taiwanese/English agent that translates content into English and Taiwanese speeches, as well as into English and Chinese texts. This transformer-based robot for computing semantic similarities employs a trained semantic Sentence-BERT (SBERT) model to analyze student-machine co-learning contents. Given the large size of the co-learning content with the ontology model, we implement a chunk-based approach for processing. This method enables effective comparison of the extensive student-provided learning content with the evaluative content from teachers and teaching assistants. Additionally, a Human Intelligence (HI)-based robot, equipped with a CI assessment mechanism based on fuzzy numbers, evaluates performance and adjusts the evaluation content of teachers and teaching assistants based on HI fuzzy numbers. Experimental results indicate that the proposed CI robot can reduce teachers' burden and objectively evaluate student-machine co-learning performance, thereby narrowing the gap in actual student-machine co-learning performance. Furthermore, it aids in assessing student-machine co-learning performance and understanding, creating a more personalized and effective learning environment.
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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 | 0.000 | 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 | 0.000 |
| 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