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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Predicting literacy intervention responsiveness using semi-supervised machine learning.

Amanda Swee-Ching Tan¹, Farhan Ali¹, Chiew Lim Lee²

¹Learning Sciences and Assessment, National Institute of Education, Nanyang Technological University, Singapore.

Research in Developmental Disabilities

|August 22, 2025

Summary

Machine learning models can predict phonics intervention success in children with special educational needs. Key predictors include verbal comprehension and memory, aiding tailored educational resource allocation.

Keywords:

Children Dyslexia Intervention Literacy Semi-supervised machine learning

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Area of Science:

Educational Psychology
Computational Linguistics
Machine Learning in Education

Background:

Phonics interventions often show limited success, focusing on phonological skills.
Predicting responsiveness for broader literacy outcomes like reading and spelling is crucial.
Machine learning offers potential for enhanced prediction of intervention outcomes.

Purpose of the Study:

To longitudinally predict systematic phonics intervention responsiveness using machine learning.
To identify key predictors of success in word reading and spelling outcomes.

Main Methods:

Applied 12 semi-supervised learning models to a dataset of 838 children with special educational needs.
Utilized a mix of labeled (intervention) and unlabeled (no intervention) data.
Included background, cognitive, and language achievement data, plus their differences, as predictors.

Main Results:

Random Forest and Gaussian Naïve Bayes models achieved the highest prediction accuracy (F1 score of 0.7).
Incorporating unlabeled data and expanded predictor sets improved model performance.
Top predictors included verbal comprehension, visual memory, and verbal working memory.

Conclusions:

Identified significant predictors for phonics intervention responsiveness.
Demonstrated the value of machine learning in predicting educational intervention outcomes.
Findings support better resource allocation, risk mitigation, and personalized interventions.