Divergent Thinking: The Performance of Students with Visual Impairments on Abstract and Scenario-Based Tasks and Their Correlates
Bibliographic record
Abstract
Introduction: Divergent thinking is a crucial component of effective problem-solving. This study investigated the divergent thinking of students with visual impairments (i.e., blindness or low vision) in three countries and explored the correlates of their performance on two tasks. Methods: A sample of 52 students with visual impairments completed two divergent thinking tasks, one traditional Alternate Uses task, and one scenario-based task created for this study. Each student’s teacher of students with visual impairments completed a questionnaire containing demographic, curriculum, and student characteristics. Results: Of the correlates examined, teacher-rated academic independence, working on grade level, number of expanded core curriculum (ECC) areas of instruction currently received, and ongoing receipt of assistive technology instruction were significantly related to task performance. Discussion: Findings demonstrate the strong relationship between real-life problem-solving, divergent thinking, and academic functioning in students with visual impairments. Further investigation is needed about the relationship between aspects of problem-solving instruction and student outcomes as they relate to divergent thinking. Implications for practitioners: Given the connection of divergent thinking to overall problem-solving ability, teachers should assess these skills in students. Using similar scenario-based real-life tasks, they can also incorporate practice opportunities for divergent thinking within ECC instruction.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".