AI-Driven Optimization of Classroom Seating: A Machine Learning Approach to Enhancing Student Performance, Collaboration, and Engagement
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The classroom seating plans should be studied as they provide information regarding learning outcomes, coopera- tion among students as well as learning achievements. Assigning students to their seats is a common practice in many classrooms and it is quite inflexible in placing students in according to their learning style, social behavior or ability meaning many children end up out of their seat and disengaged, in problems with peer interaction or learning, consequently they get low grades. To achieve this, the following K-Means clustering algorithm shall be implemented for enhancing the classification of seats in the classroom. This one combines the student’s academic performance, behavior, learning style, and the feedback from the teacher and assigns flexible organisational learning spaces based on them. This way, the system not only categorises the students for collective interaction, but also cuts on interferences and increases the organizational capacity of the classroom. For the analysis of data, we are concurrently preprocessing the data using Python and reporting the results using Streamlit. This leads to real time teaching and communication between the Instructors and students but above all it allows easy arrangement of students’ grouping and reseating. To avoid overcrowding which might affect the quality of education and the body accommodations, a ranking mechanism is established. Research suggests that by implementing seating changes based on AI, there is improved students’ involvement, group activity and even academic achievement. In this regard, teachers mentioned that most students can derive benefits from exercise some control over them in learning structures. In relation to AI, this work adds to the utilization of machine learning for the optimization of smart, learning environments. The next steps in the work to be carried out will be to apply reinforcement learning and deep learning to continue the optimization of the seating arrangements according to the activities taking place in the classroom
Index Terms—AI-Driven Classroom Management, Intelligent Seating Optimization, Educational Data Science, Student-Centric Learning Environments, Adaptive Learning Spaces, Collab- orative Learning Clusters, Machine Learning in Pedagogy, Behavioral-Aware Seating Arrangement, Academic Performance Clustering, Data-Driven Classroom Design, K-Means-Based Stu- dent Grouping, AI-Powered Student Engagement, Automated Seating Allocation, Personalized Classroom Seating, Learning Analytics for Classroom Optimization
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