The Impact of Foreign Language Anxiety on EFL Learners’ Attitudes Towards Blended Learning
Bibliographic record
Abstract
This study investigates the attitudes of Saudi English as a foreign language (EFL) learners toward blended learning and explores the impact of foreign language anxiety on shaping these attitudes. The study sample selected purposively consisted of 118 participants. A quantitative research approach was employed, utilizing a self-reported questionnaire to assess learners’ foreign language anxiety and their attitudes toward blended learning. The study yielded the following significant findings: First, the results revealed that EFL learners exhibited positive attitudes toward blended learning. Second, findings indicated that EFL learners involved in blended learning exhibited a moderate level of foreign language anxiety. Third, the study revealed that foreign language anxiety had no significant impact on the following four key constructs of attitudes toward blended learning: flexibility, study management, classroom learning, and online interaction, as well as overall attitudes. However, it was observed that the high-anxiety group exhibited more positive attitudes toward online learning and technology in blended learning compared with the low-anxiety group. These findings have pedagogical implications for educators and practitioners designing and implementing blended learning approaches in EFL classrooms. The study results recommend integrating blended learning in higher education, taking into account high-anxiety learners’ preferences of two aspects of blended learning: online learning and the integration of technology.
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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 | 0.004 |
| 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 | 0.002 | 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".