Effects of Assertive and Aggressive Communication Styles on Students’ Self-Esteem and Achievement in English Language
Bibliographic record
Abstract
The study investigated the effects of assertive and aggressive communication styles on students’ self-esteem and achievement in English language. Studies have often shown that the way a student evaluates himself/herself (self-esteem) sometimes determines their level of academic achievement. Students’ level of self-esteem could be enhanced or hindered by a number of factors which may include the communication style adopted by the teacher during classroom interaction. Hence, there is the need to conduct this study. The study adopted an expost-facto type of descriptive research design. 126 students from four purposively selected schools, and four English languages teachers participated in the study. A self-esteem questionnaire (r=0.79) and the examination scores of students were used for data collection while t-test was used for data analysis. The results indicated that there was a significant difference in the self-esteem scores of students taught by assertive and aggressive teachers in favour of the assertive group, there was a significant difference in the English language achievement of students taught by assertive and aggressive teachers in favour of the assertive group, there was also a significant difference in the English language achievement of students with high and low self-esteem. Based on these findings, it was recommended that teachers should adopt communication styles that promote positive teacher-student relationship.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".