The Application of Self-Regulated Strategies to Blended Learning
Bibliographic record
Abstract
This study analyzes vocational college students’ self-regulated strategies for blended learning. It investigates whether there are any differences in self-regulated learning strategies among students with gender and achievement variables. Twenty-three students at a vocational college in an EFL (English as a Foreign Language) context participated in the project; a structured questionnaire was used as the major research instrument and the TOEIC (Test of English for International Communication) English Test to categorize students’ competence in English. In the four subcategories of self-regulated learning strategies, the results show that the students obtained their highest scores in metacognitive and the lowest in cognitive strategies. It was observed that: a) there was a correlation between the students’ level of linguistic competence and their action control strategy; b) students with a high level of competence performed better than those with an intermediate one; c) gender was not reflected in any significant difference in any of the sub-categories but the statistic data revealed that male students had more confidence in cognitive and action control sub-categories than female students, this is potentially a field that needs further study.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 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".