Educational technologies as tools for self-regulated learning for students with autism in post-secondary settings
Bibliographic record
Abstract
The prevalence of Autism Spectrum Disorder (ASD) has been increasing globally. Educational technologies can serve as effective tools to promote learning experiences and outcomes for students with ASD. Yet, little is known about the role of educational technologies on students with ASD?s learning. Using an online survey, this dissertation study investigated the educational technologies post-secondary students with ASD (N=149) in five countries (United States, United Kingdom, Canada, New Zealand, Australia) use for their courses. Then, this study investigated the relationship between the use of educational technologies and learning outcomes as mediated by self-regulated learning strategies. This study also explored the role of autism traits on self-regulated learning for using technology and learning outcomes. Results indicate that students with ASD reported using various time management applications. A small number of students (N=12) reported using technologies to support specific needs pertaining to ASD (e.g., emotion regulation). Results indicate a positive relationship between institutional support and self-regulated learning strategies. Self-regulated learning strategies did not mediate the relationship between the use of technology and learning outcomes. Autism traits did not predict self-regulated learning strategies for using technology or learning outcomes. Findings emphasize the importance of institutional support for self-regulated learning and the importance of meaningful engagement with technologies to promote self-regulated learning and learning outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".