The Investigation of Self-Regulated Learning among Low Proficiency EFL Students
Bibliographic record
Abstract
This study aimed at investigating (1) the characteristics of self-regulated learning (SRL) among low proficiency EFL students and (2) the variation for SRL among students with different English proficiency. Participants were 85 Thai university students who were categorized as low proficiency EFL students based on their Test of English for International Communication (TOEIC) score. These students were divided into two groups: G1 and G2. The results revealed that students in the G1 group had a significantly lower TOEIC score than the students in the G2 group. For the investigation of SRL, the exploratory factor analyses (EFA) demonstrated ten subscales: Five for motivational and five for learning strategy factors. Multiple regressions that determined the influences of SRL on proficiency revealed that the students who possessed higher task value and intrinsic goal orientation were more proficient. On the contrary, if the students had higher test anxiety, they were less proficient. Students in the G1 and G2 groups differed significantly for extrinsic goal orientation. The students in the G2 group (with higher TOEIC score) had more extrinsic goal orientation than did the G1 group students. For Learning strategy in SRL, a multiple regression for learning strategy factors revealed that there were correlations between elaboration and critical thinking and proficiency of Thai EFL students. This factor was a significant predictor of the variation of proficiency. Significant differences were also found between the G1 and G2 groups in peer learning. The lower proficient group tended to rely more on their peers and seek for help. For pedagogical implications, teachers need to provide motivational environments in order to improve the mental state of low proficiency students. Attention should be paid to students who cannot perform well in class in order to raise their confidence and lessen anxiety in learning English.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".