Exploring College Students' EFL Learning Engagement in the Context of Blended Learning
Bibliographic record
Abstract
Engagement is a leading indicator of learning performance and outcomes. Blended learning in College English courses in China has been prevalent for years, yet it is still necessary to explore and enhance student interest, motivation, and outcomes through further research. Given the critical impact of academic engagement on student success, investigating how students engage in blended learning activities is important. This research presented English language learners' engagement in blended learning behaviorally, cognitively, and emotionally between online learning and in-person delivery modalities. It aimed to improve the instructional pedagogies to engage English language learners not only behaviorally, cognitively, but emotionally as well. The research aimed to help teachers gain a comprehensive understanding that will enable them to adjust current methods and identify more effective delivery strategies for improved outcomes. Learners' Engagement in Foreign Language Classroom, developed by Hiver et al. (2020), is taken in this research. In total of 223 EFL students in a Chinese university participated in this survey. The study has some main findings: in the EFL blended learning, online learning shows lower cognitive engagement but higher emotional engagement than in-person learning; blended EFL Learning engagement wasn't affected by gender or degree program levels; there are significant differences between online and in-person learning on the behavioral and cognitive engagement. According to the results, some pedagogical implications were discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".