Research on the Optimization Path of Multi-scene Teaching Strategies for English in Higher Vocational Railway Industry Based on Reinforcement Learning
Bibliographic record
Abstract
The international development of the railroad industry puts forward higher requirements for the English application ability of senior railroad students, and reinforcement learning provides new ideas for the optimization of their teaching strategies.Based on reinforcement learning, the article constructs an adaptive learning path recommendation model (RL4ALPR).The model achieves application learning of multi-scenario knowledge of English in the railroad industry through railroad English knowledge level modeling, candidate learning item screening, recommender modeling, and reward calculation.The recommended effective value of the model in this paper is 0.581 at a learning path length of 60, which is 7.79% to 13.70% higher than the control model.The model realizes accurate recommendation of English exercises for the railroad industry based on the answers to the exercises.The evaluation scores of the students in the experimental class under the intervention of the model in this paper are improved to 24.26, 17.50, and 19.64 for speaking, reading comprehension, and translation of English in the railroad, respectively.Under the model of this paper, English teaching in the higher vocational railroad industry is highly recognized by students in terms of "content setting", "teaching quality" and "teaching effect".And the experimental class is better than the control class in terms of the level of knowledge about English for the railroad industry, the application of English for the railroad industry in multiple scenarios, and the comprehensive ability evaluation scores of 4-5 points more than the control class.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".