The Effectiveness of Teacher Autonomy Supportive Style on Enhancing Student Engagement in EFL Virtual Classrooms
Bibliographic record
Abstract
Promoting students’ engagement in classrooms is among the most significant challenges faced by teachers in virtual classrooms. Prior research has investigated the effectiveness of using teacher autonomy supportive style (TASS) during in-person classes (Jang et al., 2010; Li et al., 2020; Núñez & León, 2019; Reeve et al., 2004). However, limited research has been conducted in virtual classrooms (Bedenlier et al., 2020; Chen & Jang, 2010; Chiu & Hew, 2018). Ryan and Deci (2020), suggested that further research should focus on student engagement within virtual classrooms. Moreover, although EFL teachers often struggle to engage their students (Susanti, 2020), the majority of the related studies have been carried out in various learning contexts (Jang et al., 2010; Li et al., 2020; Shih, 2008). Most of this limited body of literature in the EFL context is composed primarily of quantitative research collected through cross-sectional study designs. Evidence suggests that this gap can be addressed by conducting well-designed qualitative studies investigating student engagement (Fredricks et al., 2016; Harris, 2011; Zyngier, 2008). Thus, there is an urgent need for research that tackles these gaps effectively.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".