Bibliographic record
Abstract
The integration of artificial intelligence technology with modern network communication technology in an educational quantification system holds significant importance for enhancing the quality of classroom learning for students. In many vocational school education systems, teachers often act as knowledge transmitters. In traditional classrooms, it is often challenging for teachers to efficiently obtain the learning progress of each student. Due to the structure of the curriculum, students' classroom learning situations typically have to be assessed through a combination of assignments and end-of-term exams. This makes it difficult for teachers to promptly correct students' erroneous learning methods. These issues render many students who are trained through vocational education less adaptable to modernized societal production. This article takes the Shanghai Science and Technology Management School as a typical case and, based on classroom teaching theory, proposes a design and implementation method for an instructional platform that integrates artificial intelligence technology and network communication technology. The system design utilizes artificial intelligence technology for behavior and facial expression-based classroom teaching supervision and combines it with an automated assignment grading system to generate accurate analytical reports on students' classroom learning situations. Research indicates that using this system accurately analyzes students' learning situations during assignment completion, effectively enhances teachers' understanding of students' learning quality, and reduces teachers' burdens in classroom teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".