Utilization of Online and Offline Mixed Education Model in ESP Teaching—Taking Financial English Teaching as an Example
Bibliographic record
Abstract
With the deepening of educational informatization, especially the emergence of Massive Open Online Course (MOOC), the blended online and offline teaching mode has brought profound impact on classroom teaching and greatly improved student learning efficiency. This article attempted to apply this new teaching model to financial English classrooms and organically combined the ESP (English for Specific Purposes) teaching method with online and offline blended teaching methods to improve students' English learning abilities. In order to obtain objective and accurate teaching experiment results, based on the principle of comparison, students from undergraduate English classes majoring in economics were selected as the statistical samples for the experiment, and they were compared and analyzed to minimize differences between samples. Before the experiment, a score test was used to determine the experimental subjects. In the comparison of grades using different teaching methods, online and offline teaching scored 75.3 points; ESP teaching scored 84.2 points; this article's teaching scored 93.7 points. This article helps to enhance students' interest in financial English classes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".