The Impact of Self-Efficacy, Learning Preferential, Learning Motivation and Academic Achievement on EFL Students in Saudi Arabia
Bibliographic record
Abstract
The aim of this study is to examine the effect of self-efficacy moderated by e-learning preferential on the academic achievement through the mediation of learning motivation. A survey approach was used. 264 EFL students were randomly selected from three different universities in central Saudi Arabia during the academic year of 2020/2021. A 5- likert scale questionnaire was applied with two instruments (for self-efficacy and for learning motivation), in addition to a section for the demographic variables that included sex, age, study level, learning preferential, and grade point average (GPA). IBM SPSS AMOS (23) was employed, where structural equation modeling, SEM (CB-SEM), was applied as well as Macro Process Hayes Plug-In. The analysis used both the measurement and structural model. Reliability was tested with alpha Chronbach coefficient and the model fitness was evaluated using several criteria including regression weight, goodness of fit indices (GFI). The finding showed that student in the scored high level of self-efficacy and learning motivation, though the former is higher. In addition, there was a significant direct effect on GPA for self-efficacy and learning motivation. However, a new significant direct effect appeared, because of the interaction between self-efficacy and the learning preferential. More importantly, in addition to the mediated effect of the self-efficacy on the GPA through the learning motivation, there appear to be another moderated mediated effect which is the indirect effect of the interaction between self-efficacy and the learning preferential on the GPA through the mediation of the learning motivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".