Practical Application of Fuzzy Atmosfield with Machine Learning for Group Learning Assessment in Smart Classrooms

Traditional classroom group learning state evaluations are labor-intensive, time-consuming, and often biased, which has sparked the need for an automatic group learning state assessment method. Current research on smart education focuses primarily on identifying individual student behaviors, which has left a gap in the assessment of the group learning states. To address this, an integrated machine learning method with a Fuzzy Atmosfield for group learning state assessment is proposed. The Fuzzy Atmosfield was designed to capture the learning state of the group using an improved three-axis vector. The proposed method was tested on a customized simulated classroom dataset. Subsequently, it was applied to a real classroom video dataset. The accuracy of behavior recognition in the real classroom video data reached 83.73%, and the analysis results corresponded to real classroom situations. The experimental results show that the proposed method can provide automatic, accurate, and real-time group learning state assessments in a smart classroom.