An Analysis on the Application of Interactive Teaching Approach in College Oral English Class
Bibliographic record
Abstract
How to cultivate students’ communicative competence has become one of the core contents of current college English teaching. Facing the new challenge, Interactive Teaching Approach emerged and has been favored and advocated by domestic and foreign education scholars. Based on the theory of Constructivism, Krashen’s Input Hypothesis and Interaction Hypothesis, this paper conducts a qualitative analysis of previous studies tentatively. By studying the actual situation of college oral English teaching and the current research status of the Interactive Teaching Approach, this paper analyzes the application status and its advantages. The study indicates that the domestic college English teaching environment should be improved and teachers fail to give students adequate guidance and sufficient training. This study also finds that Interactive Teaching Approach is effective to improve college students’ communicative competence based on previous researches. The results show Interactive Teaching Approach contributes to lighten students’ learning passion, promote the interactive teaching climate and improve the efficiency of college oral English teaching. The author hopes that this study could help the application and popularization of the Interactive Teaching Approach in college oral English teaching, and contribute to the reform of college oral English teaching.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".