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

Abstract

The availability of educational data in novel ways and formats brings new opportunities to students with special education needs(SEN),whosebehavior andlearning arehighlysensitiveto their body conditions and surrounding environments. Multi modal learning analytics (MMLA) captures learner and learning environment data in variousmodalitiesandanalysesthemtoexplain 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.