VIDEO-BASED ABNORMAL DRIVING BEHAVIOUR DETECTION VIA DEEP LEARNING FUSIONS

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 . 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 thatby 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.