Using Self-Regulated Learning Strategies in Blended Classrooms to Improve Students’ Receptive Language Proficiency
Bibliographic record
Abstract
The Self-Regulated Learning (SRL) Strategies have served as a basis in blended classrooms over a period of time to conduct experiments on various students’ problems. The technique of blended classrooms has shown optimistic results and has offered new opportunities for improving students' reading and learning skills, mainly known as receptive language proficiency. This study investigated how integrating SRL strategies within a blended classroom enhances English as a Foreign Language (EFL) learners' reading comprehension and writing skills. A quasi-experimental research design was employed, with participants divided into an experimental group, which utilized SRL strategies, and a control group, which did not. The findings revealed that the experimental group significantly outperformed the control group in both writing and reading comprehension skills. The mean scores for the experimental group were 84.30 for writing skills and 87.20 for reading comprehension, compared to 66.70 and 70.10, respectively, in the control group. These statistically significant differences, with p-values less than 0.001, confirm that SRL strategies substantially enhance students' receptive language proficiency in a blended EFL classroom. Furthermore, SRL strategies effectively improved reading comprehension at all levels—literal, inferential, and critical—highlighting their comprehensive impact on students' cognitive development. This study highlights the importance of incorporating SRL strategies in blended learning environments to improve EFL learners' language proficiency, offering a valuable approach for educators aiming to enhance students' reading and writing capabilities.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".