Identifying Determinants of Dyslexia: An Ultimate Attempt Using Machine Learning.
Sietske Walda1, Fred Hasselman1, Anna Bosman1
1Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.
Frontiers in Psychology
|April 25, 2022
Summary
Cognitive skills do not reliably predict reading problems or future reading progress, despite correlations. Traditional and machine learning models showed poor predictive accuracy for reading development.
Area of Science:
- Cognitive Psychology
- Educational Psychology
- Computational Linguistics
Background:
- Traditional linear models struggle to capture complex interactions in reading development.
- The role of cognitive skills in reading difficulties remains unclear due to systemic factors.
Purpose of the Study:
- To investigate the predictive role of cognitive skills in current reading level.
- To assess the predictive power of cognitive skills for future reading progress.
Main Methods:
- Comparison of four supervised machine learning techniques against traditional General Linear Models.
- Studies involved average school-aged children and poor readers.
Main Results:
- Both machine learning and traditional models yielded poor to acceptable, but inadequate, predictive results for reading development.
- Cognitive skills showed correlations with each other but were not found to be predictive of reading problems.
Conclusions:
- Cognitive skills may not be direct predictors of reading problems.
- Findings challenge existing theories on reading development and have implications for intervention strategies.
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