AI-BASED MULTIMODAL LEARNING ANALYTICS FOR PREDICTING BEHAVIORAL CHANGES IN STUDENTS WITH SPECIAL EDUCATION
Keywords:
Multi modal learning analytics (MMLA), special education needs (SEN)Abstract
The present project is aimed at forecasting behavioral changes in the students with Special Education Needs (SEN) based on the analysis of their learning activities and interactions. Classroom activity, accomplishment of tasks, attendance, and teacher observations are all gathered and handled in order to come up with trends in student behavior. The machine learning model predicts possible positive or negative behavior change to support the teacher and caregivers to offer prompt assistance and intervention. It also contains basic visualization, like dashboard or charts, so that it is easier to have the educators see the trends of behaviors at a glance. With a simple data-driven model, the project permits the
behavior in the classroom to be managed in advance, promotes engagement, and facilitates
better learning the results in a practical and readily constructible way.
