Transformer-Based Semantic SBERT Robot with CI Mechanism for Students and Machine Co-Learning
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".