The Role of Blended Learning on Moderating Self-Motivation to Mitigate Foreign Language Anxiety among EFL Students
Bibliographic record
Abstract
The purpose of this study is twofold: (1) it looks at how much foreign language anxiety and self-motivation there are among EFL students, and (2) to determine if learning preferences interact with self-motivation to lessen FLA, i.e., moderate this relationship. To do this, 232 EFL students from the 2020–2021 academic year were randomly chosen to participate in a survey method at three universities in central Saudi Arabia: Qassim University, Majmaah University, and Shaqra University. SPSS was used for descriptive analysis, where Macro Process Hayes Plug-In was used for the moderated regression analysis. The findings showed a moderate level of internal and extrinsic goal orientation, control over learning beliefs, self-efficacy, task value, social engagement, instructor support, as well as anxiety related to learning a foreign language. Additionally, it was determined that participant demographic factors had no statistically significant impact on any of the distinct dimensions of self-motivation or anxiety related to learning a foreign language. More significantly, blended learning was found to have a greater negative impact on foreign language classroom anxiety than face-to-face learning and e-learning, indicating that it has a greater impact on increasing self-motivation to lessen classroom anxiety.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".