PAD Class Teaching in Undergraduate English Courses in the Era of Information Technology
Bibliographic record
Abstract
We studied the teaching effect of Zhang’s Presentation - Assimilation – Discussion (or PAD) class mode in undergraduate English class. Taking undergraduates of Lanzhou University of Arts and Sciences in China as an example, we studied the teaching of undergraduate English using PAD in the information technology era. The new PAD mode has three well defined components: teacher presentation, student assimilation and discussion. Using experimental and control oral English classes, with similar input skill, after eight weeks, we found the English proficiency of both groups increased, but the PAD class was significantly better in scores for content and expression, With PAD style teaching, the experimental group improved their mean scores from 74.2 to 80.1 (median 74 to 81.5), whereas the traditional class only improved from 75.4 to 76.8 (median 74 to 79). A t-test confirmed that the results were significant.Assessments, based on questionnaires and interviews, also showed that PAD class teaching improved attitude and learning habits. After the experiment, interviews with some students in the experimental class further confirmed this. Thus the improved learning effect is worth applying in more classrooms to improve undergraduate English ability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".