LD’s on the Brain: Teaching Students with Learning Disorders about their Unique Brains and Learning Profiles
Bibliographic record
Abstract
This study examined the feasibility of implementing "Brain Building 101" as a psychoeducational feedback tool to help middle school students with Learning Disorders (LDs) understand their learning profiles. Using a case study approach, four 8th-grade students engaged in a two-session intervention designed to help students understand how they learn and identify strategies to support their learning. This intervention was designed using Growth Mindset principles within a Therapeutic Assessment framework to teach participants key concepts about brain function while collaboratively exploring their cognitive strengths and challenges. Results indicated that participants engaged well with and learned from the intervention, demonstrating increased self-awareness with a greater ability to articulate their strengths, challenges, and preferred learning styles. Participants demonstrated increased understanding of how they learn and were able to identify effective strategies for managing academic tasks. Results indicate that participants showed increased self-esteem by adopting growth mindset principles and reframing their learning profiles using strength-based language. Advocacy skills showed some improvement, with students expressing confidence in seeking support next year in high school. This study underscores the importance of teaching students with LDs about their learning profiles and equipping them with the knowledge and tools to navigate academic challenges. The findings highlight the potential and the value of structured, student-centered interventions in fostering meaningful change to participants’ self-awareness, self-esteem, and advocacy skills.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".