Procrastination and Motivation of Undergraduates with Learning Disabilities: A Mixed‐Methods Inquiry
Bibliographic record
Abstract
The purpose of this mixed‐methods article was to report two studies exploring the relationships between academic procrastination and motivation in 208 undergraduates with ( n = 101) and without ( n = 107) learning disabilities (LD). In Study 1, the results from self‐report surveys found that individuals with LD reported significantly higher levels of procrastination, coupled with lower levels of metacognitive self‐regulation and self‐efficacy for self‐regulation than those without LD. Procrastination was most strongly (inversely) related to self‐efficacy for self‐regulation for both groups, and the set of motivation variables reliably predicted group membership with regard to LD status. In Study 2, individual interviews with 12 students with LD resulted in five themes: LD‐related problems, self‐beliefs and procrastination, outcomes of procrastination, antecedents of procrastination, and support systems. The article concludes with an integration of quantitative and qualitative results, with attention paid to implications for service providers working with undergraduates with LD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".