Analysis of the Interrelatedness of Self -Regulation, Learners’ Engagement, and Self-Perceived Development in a Synchronous Online EFL Reading Course
Bibliographic record
Abstract
Self- regulated learning (SRL) and engagement have been postulated as important traits for success in online learning. However, little is known about how these constructs and their subconstructs are related and how they impact learners’ self-perceived success particularly with receptive English skills in EFL contexts. This study was conducted to address this gap in the literature through assessing the impact of SRL on 55 EFL Saudi learners’ levels of engagement in and self-reported satisfaction with an online EFL course. Results from regression and correlation analyses revealed the importance of self-regulatory skills in enhancing learners’ engagement and perceived success in an online reading course. The results also highlighted the importance of considering the subconstructs of both engagement and self-regulation in understanding their relationship and their overall relatedness to self-reported success in an online course on reading comprehension. The findings also point to the need for a unified definition of the two constructs and the significance of considering the distinct contribution of their subconstructs. Pedagogical and theoretical implications are discussed in light of the study’s findings.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".