Using Corpus and Artificial Intelligence Technology to Build English Conversation Classroom Teaching Scenarios
Bibliographic record
Abstract
This paper analyzes the current teaching development direction of intelligent simulation context, and argues that the development and updating of semi-open human-computer dialogue systems based on scenes and topics need to rely on the division of labor between machines and human beings.In this regard, the development strategy of intelligent teaching of English dialogue interaction is proposed by combining the speech corpus annotation system designed based on artificial intelligence technology and the dialogue interaction teaching strategy derived from the interactive teaching model.The corpus annotation model of multi-layer perceptual machine is designed, which consists of real-time interaction module, core technology and algorithm module and data storage module.Draw a mind map of the framework for analyzing the effectiveness of dialog teaching, and develop a dialog interactive teaching strategy for pre-class dialog, in-class dialog and after-class dialog by invoking the interactive teaching model.Analyze the annotation results of the intelligent annotation model of speech corpus in LDC corpus and UN corpus.Observe and organize the teaching implementation effect of the dialogue interactive teaching strategy, and illustrate the pedagogical feasibility of the English dialogue interactive intelligent teaching development strategy proposed in this paper in combination with the learning achievement and questionnaire results.In the classroom English conversation, the zero feedback of dialog teaching is only 1.60%.The linguistic feedback of dialogue is reflected in the proportion of 33.87% of combing and summarizing, 33.55% of judging the correctness and error, and 30.99% of extending and pursuing questions.The effectiveness of English conversation classroom is improved.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".