Executive Function and Planning Features of Students With Different Types of Learning Difficulties in Chinese Junior Middle School
Bibliographic record
Abstract
The aim of the present study was to investigate the executive function and planning features of students with different types of learning difficulties. Students with mathematics difficulty (MD; n = 17), reading difficulty (RD; n = 12), and their commonalities (MDRD; n = 22), along with typically academically developing peers (TD; n = 22), were evaluated on an array of cognitive measures (working memory, inhibition, and planning) individually. Results revealed significant differences among groups on various cognitive measures. Students in the MD, RD, and MDRD groups showed poorer performance compared to the TD group on all of the working memory, inhibition, and planning tasks. The MDRD group showed an overall weakness when compared to other groups, indicating severe cognitive deficits in students with MDRD. The RD group showed deficits in inhibition and planning on tasks requiring verbal skills; MD students showed deficits in inhibition and planning on digit-related tasks. However, no salient difference was found among the MD, RD, and TD groups on working memory. Results have implications for understanding the cognitive features of MD, RD, and MDRD. Intervention programs targeting inhibition and planning may be beneficial for improving reading and mathematics achievement in students with learning difficulties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".