The Characteristics of Excellence and Success Factors in Self-Regulated Learning among National Award-Winning Upper Secondary Students in ROIKEANSARASIN Cluster
Bibliographic record
Abstract
This research was a qualitative phenomenological study and its purposes were to 1) understand the meaning of self-regulated learning and excellence from the perspectives of students, administrators, teachers, and parents of students who have achieved learning success. 2) study the self-regulated learning process of successful students through the experiences shared by students, administrators, teachers, and parents. 3) identify the success conditions of self-regulation in students who have achieved learning success, as obtained from students, administrators, teachers, and parents. The research instruments consisted of causal factor interview protocol and observational forms, and the data was analyzed by using content analysis. The research findings indicated that 1) self-regulated learning is a process of self-control that involves taking responsibility for learning tasks, having the motivation to seek knowledge using various skills, self-assessment, management, and having the necessary reinforcements to achieve one's goals. This leads to excellence, a unique quality that surpasses others, and it stems from individual dedication to personal development and setting challenging standards for oneself. 2) The processes or strategies to promote self-regulation in learning and lead to excellence consist of three components: cognition, metacognition, and motivation. 3) The conditions or factors that promote self-regulated learning and contribute to student success include personal factors, behavioral factors, and environmental factors. These three factors directly affect the learners and enable them to self-regulate their learning and develop themselves towards excellence. The findings of this study can serve as a basis for further development of self-regulated learning to guide students towards excellence.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".