Emotional Linkage among Teacher-Student in English Multimedia Smart Classroom Teaching in the Internet of Things Big Data Era
Bibliographic record
Abstract
At this stage, multimedia has been increasingly widely used in college English education with the advantages of rich content, convenient updating and various forms, providing rich and novel resources for college English teaching. However, compared with the traditional classroom, the multimedia smart classroom relatively reduces the linkage between teachers and students, making it difficult to form emotional linkage between teachers and students in the classroom teaching situation, which seriously affects the teaching effect. In view of this situation, this paper studied the English multimedia smart classroom in the era of Internet of Things big data and the emotional linkage between teachers and students in classroom teaching. This paper conducted experiments and analysed on the emotional linkage between teachers and students from five aspects: students' learning enthusiasm, learning efficiency, academic level, teachers and students' extra-classroom linkage, and teachers' satisfaction. The experimental results showed that under the condition of active emotional linkage between teachers and students, students' English learning enthusiasm has increased by 6.76%, students' English academic level has increased by 4.08%, and students' satisfaction with teachers has increased by 4.92%. Positive emotional linkage between teachers and students can effectively improve students' English learning enthusiasm and English learning performance, and can increase students' recognition of teachers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".