Predicting behavior change in students with special education needs using multimodal learning analytics

Predicting behavior change in students with special education needs using multimodal learning analytics

• Chan, Rosanna Yuen Yan The Chinese University of Hong Kong
• Wong, Chun Man Victor The Education University of Hong Kong
• Yum, Yen Na Cherry The Education University of Hong Kong

IEEE Access, 11, 63238-63251, 2023

IEEE

2023

• E Special Education
  • ED Education for Students with Behavioural Problem

Applied Behavior Analysis (ABA) Multimodal Learning Analytics (MMLA) Predictive Modeling Special Education Needs (SEN)

• Hong Kong

• 1997.7 onwards

• Unknown or Unspecified

• View Full Text 全文連結

The availability of educational data in novel ways and formats brings new opportunities to students with special education needs (SEN), whose behaviour 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’ behaviour change upon their participation in applied behaviour analysis (ABA) therapies, where ABA therapy is an intervention in special education that aims at treating behavioural problems and fostering positive behaviour changes. Here we show that by inputting multimodal educational data, our machine learning models and deep neural network can predict SEN students’ behaviour 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. Copyright © 2023 IEEE.

• English

Journal Articles

• 21693536

• 10.1109/ACCESS.2023.3288695

https://bibliography.lib.eduhk.hk/en/bibs/cb75b0b2

2023-10-18