Educational technologies as tools for self-regulated learning for students with autism in post-secondary settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it