Identifying the Cognitive and Emotional Components of Self-Advocacy in Exceptional Learners
Bibliographic record
Abstract
This study aims to identify the cognitive and emotional components of self-advocacy in exceptional learners and examine the external factors that influence their ability to advocate for themselves in educational settings. A qualitative research design was employed, utilizing semi-structured interviews with 29 exceptional learners recruited through online platforms. Theoretical saturation was reached, ensuring comprehensive data collection. NVivo software was used to conduct thematic analysis, identifying key cognitive and emotional factors related to self-advocacy. Participants shared their experiences regarding metacognitive awareness, decision-making, emotional regulation, and external support systems, providing rich qualitative data for analysis. The results indicated that self-advocacy among exceptional learners is shaped by cognitive competencies, including metacognition, decision-making, and information-seeking behaviors, as well as emotional factors such as self-efficacy, resilience, and emotional regulation. Participants who demonstrated strong metacognitive strategies and confidence were more effective self-advocates, whereas those facing anxiety or low self-efficacy struggled to assert their needs. External factors, such as educator support, family involvement, and institutional policies, played a critical role in shaping advocacy experiences. Online networks and digital advocacy resources were identified as valuable tools for enhancing self-advocacy skills. However, institutional barriers, including bureaucratic challenges and lack of awareness among educators, posed significant obstacles for learners. The study highlights the complexity of self-advocacy in exceptional learners, emphasizing the interplay between cognitive awareness, emotional resilience, and external support structures. Enhancing self-advocacy skills requires targeted interventions, including advocacy training, institutional support, and digital resource accessibility.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".