Difference between revisions of "Learners with Disabilities"
Jump to navigation
Jump to search
m |
m |
||
| Line 4: | Line 4: | ||
* Prediction is more accurate for students with Disabilities than students without Disabilities | * Prediction is more accurate for students with Disabilities than students without Disabilities | ||
Loukina & Buzick (2017) [https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ets2.12170 pdf] | |||
Loukina & Buzick (2017) [https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ets2.12170 pdf] | |||
* a model (the SpeechRater) automatically scoring open-ended spoken responses for speakers with documented or suspected speech impairments | * a model (the SpeechRater) automatically scoring open-ended spoken responses for speakers with documented or suspected speech impairments | ||
Revision as of 19:36, 28 June 2023
Permodo et al (2023) pdf
- Paper discusses system that predicts probabilities of on-time graduation
- Prediction is more accurate for students with Disabilities than students without Disabilities
Loukina & Buzick (2017) pdf
- a model (the SpeechRater) automatically scoring open-ended spoken responses for speakers with documented or suspected speech impairments
- SpeechRater was less accurate for test takers who were deferred for signs of speech impairment (ρ2 = .57) than test takers who were given accommodations for documented disabilities (ρ2 = .73)
Riazy et al. (2020) pdf
- Models predicting course outcome of students in a virtual learning environment (VLE)
- Disparate impact was found for students with self-declared disabilities, with systematic inaccuracies in predictions for learners in this group.