The Relationship Between Self-Regulations and Online Learning in an ESL Blended Learning Context
Bibliographic record
Abstract
Technology has changed the social landscape and the nature of social interaction. Education has been affected by these changes, as technology has facilitated the creation and Implementation of new educational environments and delivery methods (e.g., distance and blended-learning structures). While the prevalence of such approaches has increased over time, little is known about the learning skills that promote academic success; consequently, the ability of instructors and administrators to implement appropriate methods to foster these skills is hindered. This study examines distance education and focuses on the self-regulation literature to identify skills that can predict ESL learning success in a blended educational environment. Data were collected from 70 students attending a blended English language course. Using subscales from Motivated Strategies for Learning Questionnaire, five Self-regulatory attributes likely to predict academic performance were identified: intrinsic goal orientation, self-efficacy for learning and performance, time and study environment management, help seeking, and Internet self-efficacy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".