Factors Influencing Speaking Proficiency Improvement in EFL Students under SPOC-based Blended Learning
Bibliographic record
Abstract
This study examines the impact of a SPOC-based blended learning model on enhancing EFL learners' willingness to engage in speaking practice, with a particular focus on Chinese university students. Although English education has gained prominence in China, many students still face significant challenges in developing spoken proficiency due to a lack of practice opportunities and high levels of anxiety. By integrating insights from the Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM-ISC), this research addresses these issues by demonstrating how the SPOC blended learning approach can effectively support language learning. Using a sample of 396 students from Chengde, Hebei Province, data analysis through SPSS and AMOS showed that the SPOC model enhances learners' confirmation and perceived usefulness. This increased perception of usefulness and alignment with expectations contributes to a reduction in anxiety, ultimately leading to a greater willingness to participate in speaking activities. As a result, the SPOC model not only improves learner engagement but also provides a practical and scalable solution for overcoming key obstacles, such as anxiety, in EFL speaking practice. These findings highlight the model’s potential as an effective approach for fostering spoken English proficiency in higher education settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".