PREDICTING BEHAVIOR CHANGE IN STUDENTS WITH SPECIAL EDUCATION NEEDS USING MULTIMODAL LEARNING ANALYTICS

Abstract

The availability of educational data in novel ways and formats brings new opportunities to students with special
education needs (SEN), whose behavior and learning are highly sensitive to their body conditions and surrounding
environments. Multimodal learning analytics (MMLA) captures learner and learning environment data in various
modalities and analyses them to explain the underlying educational insights. In this work, we applied MMLA to
predict SEN students’ behavior change upon their participation in applied behavior analysis (ABA) therapies, where
ABA therapy is an intervention in special education that aims at treating behavioral problems and fostering positive
behavior changes. Here we show that by inputting multimodal educational data, our machine learning models and
deep neural network can predict SEN students’ behavior change with optimum performance of 98% accuracy and
97% precision. We also demonstrate how environmental, psychological, and motion sensor data can significantly
improve the statistical performance of predictive models with only traditional educational data. Our work has been
applied to the Integrated Intelligent Intervention Learning (3I Learning) System, enhancing intensive ABA therapies
for over 500 SEN students in Hong Kong and Singapore since 2020.