Construction of English Teaching Index Evaluation System Based on Artificial Intelligence and Multimedia Technology
Bibliographic record
Abstract
AI (Artificial Intelligence) technology and multimedia technology are changing with each passing day, and have gradually involved in various fields.At the same time, the use of these technologies in the field of education has also effectively promoted the assessment of English classroom teaching.Since good classroom teaching is inseparable from evaluation, then scientific and reasonable teaching assessment of English classroom teaching can be guaranteed.At present, there is a lack of English teaching index assessment system for AI and multimedia technology.Therefore, this paper conduced in-depth research on improving the assessment system of English classroom teaching, and expounded on AI and multimedia technology.This paper built an assessment system for English classroom teaching based on AI and multimedia technology, and innovated and improves the assessment system.The experiment showed that 85% of the teachers were satisfied with the assessment system of English teaching indicators based on AI and multimedia technology, and 70% of the teachers were satisfied with the assessment system of traditional English teaching indicators.The new system can help to promote a more objective and scientific assessment of English 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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".