The Reading Challenges, Strategies, and Habits of University Students With a History of Reading Difficulties and Their Relations to Academic Achievement
Bibliographic record
Abstract
Given the increase in students with learning disabilities entering university, we investigated a broader group—students with a history of reading difficulties (HRD)—who are known to be at risk of academic struggles. We identified the self-reported reading challenges and strategies of university students with HRD ( n = 49) and those with no history of reading difficulties (NRD; n = 88) and examined group differences and relations with first-year grade point average (GPA). Students with HRD reported more difficulties with perceived reading comprehension, concentration, and reading speed than students with NRD. Groups differed in use of reading strategies: Students with HRD were descriptively more likely to reduce reading volume by using alternative materials and chose to read based on text length and availability of alternative materials. For both groups, reading completion and concentration strategies were positively related to GPA, while perceived difficulty with reading comprehension and choosing to read based on interest were negatively related to GPA. Some strategies were negatively associated with GPA for students with NRD, but not for students with HRD. Findings revealed the challenges that students with HRD experience with reading in university and identified strategies, potentially adaptive or maladaptive, that they used to manage their academic reading load.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".