Investigating Teacher-Student Emotional Dynamics in English Language Teaching: Examining Co-Regulation Techniques and Their Impact on Learning Outcomes
Bibliographic record
Abstract
The main objective of this research is to explore the role of teacher-student emotional dynamics within the context of English language teaching. Co-regulation refers to the ability of one person to help another person regulate their emotions and behavior. In the context of the classroom, this means that teachers and students can work together to create a conducive environment where everyone can learn. This study explores how different methods of co-regulation used by English language teachers affect learners’ feelings, motivation levels, and academic achievement in English lessons. They include: affect copy, overt adult praise, decision making by consensus and emotion prompting scaffolding which the study used to maintain the level of students’ engagement and achievement in English language learning. The study targeted 50 ELT teachers in the 15 selected schools, and 450 students in Junior High, High and Senior High in city of Multan, Pakistan. The sampling procedure was done through stratification in an effort to sample according to grades, the type of ELT contexts, and students’ demographic profile. The techniques used consisted of direct and proximal observation of ELT context activities, completion of questionnaires and evaluation of students’ records. The findings prove that emotional co-regulation processes improve the ELT environment and students’ self-regulation skills as well as their motivation in learning English. The relationships found between teachers’ self-identified emotional literacy and the degree to which co-regulation strategies were purposefully enacted highlighted the significance of emotional intelligence in the school context. The research implies the significance of teachers’ emotional awareness and their ability to utilize specific co-regulation interventions to create a healthy and conducive environment within English language 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".