The Effects of STAD Method on Chinese Students’ Motivation in Learning English Communicative Competence
Bibliographic record
Abstract
Many studies revealed that students have difficulties in learning English communicative competence and some are even resistant in learning the language. This study aimed to examine the effects of the STAD method in students’ motivation towards learning English communicative competence. The study adopted a quasi-experimental design lasting for 8 weeks which compared the performance between the Experimental Group taught using the STAD method and the Control Group taught using the conventional method. The sample were 80 first-year non -English major students from two polytechnic colleges in Guangdong, China. The samples were chosen as intact-groups (40 students in the Experimental Group and 40 students in the Control Group). The questionnaire on learning motivation was adapted from Keller’s ARCS model. The questionnaire was used to gauge student’s motivation in learning English communicative competence before and after the intervention. SPSS (Statistical Pakage for Social Sciences) Program for Windows version 25 was applied to analyze the data using the independent-samples t-test. The findings of this study revealed that the students in the Experimental Group showed significantly higher motivation in learning English communicative competence than the Control Group in their overall motivation and in all the ARCS components (Attention, Relevance, Confidence and Satisfaction). This empirical study has strong pedagogical implication as it suggests that STAD method can be used by English lecturers to enhance students’ motivation in learning English communicative competence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".