Self-Regulation in the Learning Process: Actions through Self-Assessment Activities with Brazilian Students
Bibliographic record
Abstract
Learning a foreign language is, among other factors, based on the perception of one’s own development and on undertaking strategies for greater communicative competence, which are founded in autonomous procedures that span the necessity for greater responsibility. As learning a language demands constant study, even after the school period – when there are no more teachers as mediators - the development of self-regulated learning skills becomes relevant. One option revealed in literature as a trigger for greater autonomy for learning, is the use of self-assessment. This study aimed at identifying and analyzing the potential of self-assessment for students to plan strategies and control their own actions, procedures which are part of self-regulated learning practice. The research adopted a qualitative approach in the form of a case study, and involved 25 students in the 8th grade of a public school in Northern Paraná, Brazil. Information from the instruments for data collection was subjected to thematic content analysis. The results reveal that self-assessment encourages the use of strategic planning and monitoring of one’s own learning only if joined to teacher intervention and motivational strategies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".