Research on the Optimization Path of Classroom Management for Foreign Language Teachers in Colleges and Universities Based on AIGC and the Construction of Its Multilevel Decision-Making Model
Bibliographic record
Abstract
AIGC-driven development and innovation of regional education has become an important issue, and in the context of the era when AIGC technology has triggered profound changes in education, the traditional education model is experiencing a paradigm shift from the transmission of knowledge to the cultivation of innovation ability.Based on this, we first construct a model of influencing factors in the application of AIGC in course management based on the rooting theory, and verify the proposed hypotheses to provide a theoretical basis for the construction of course management optimization and multi-level decision-making model.Then we optimize the course management of foreign language teachers in colleges and universities by relying on the all-round and multi-level innovation of AIGC in the field of education, and construct a multi-level decision-making model.In the teaching application practice, the scores of the experimental class on learning interest, learning attitude and learning motivation are all higher than 75 points after practice, and the average score is 8.87 points higher than that of the control class, and the P is less than 0.05.The learning achievement of the experimental class is increased from 73.95 to 80.95 (P < 0.05), and the optimized multilevel decision-making model of this paper has a significant effect on improving students' learning interest, learning attitude, learning motivation and learning achievement, learning attitude, learning motivation as well as learning achievement, which further validates the application effectiveness of the multilevel decision-making model and provides case references for researchers of AIGC-based instructional decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".