This research introduces a multi-class adaptive active learning framework to predict student anxiety, aiming to enhance early intervention and support mechanisms in educational environments. Traditional anxiety prediction models often fall short due to limited labeled data and static learning processes. Our approach leverages adaptive active learning to iteratively select the most informative data points for labeling, improving model accuracy and robustness. By incorporating multi-class classification, the model differentiates between various levels of anxiety, providing a nuanced understanding of student mental health. Experimental results demonstrate the effectiveness of the proposed method, showing significant improvements in prediction accuracy over baseline models. This study underscores the potential of adaptive active learning in educational data mining, offering a scalable solution for real-time anxiety prediction and contributing to more responsive and supportive educational systems.

Keywords:   Adaptive Active Learning,Multi-Class Classification,Student Anxiety Prediction,Educational Data Mining,Machine Learning in Education,Mental Health Assessment,Real-Time Analytics