DefinePK

Abstract

Student success is a crucial metric for educational institutions, as it affects not only the individual student's academic and personal growth but also the institution's reputation, funding, and overall educational outcomes. Student failure is a common and concerning issue in educational institutions, often leading to significant academic and personal consequences. This research explores the potential of deep learning techniques to accurately predict student emotions and prognostication against failure, enabling proactive interventions. Prior studies in this field have mostly depended on conventional machine learning techniques to predict student performance and detect student risk, such as logistic regression and linear support vector machines. However, advanced techniques are frequently needed to capture the underlying patterns and correlations due to the complexity and richness of student behavior and performance. Within the field of artificial intelligence, deep learning has proven to be extremely effective in some applications, such as computer vision, generative AI, and natural language processing. Toward this goal, this research offers an extensive deep learning architecture that utilizes the vast data from the class environment to forecast student emotions more precisely and finely. For evaluating the above facts, the deep learning model CNN-LSTM is used to reveal an effective participation evaluation of students' attention and involvement during classroom sessions. By creating and assessing cutting-edge AI models that improve the precision, flexibility, and real-time capabilities of teaching aids, this research seeks to overcome these constraints. Furthermore, the proposed model demonstrates an accuracy of 91% making it a highly useable real-time application.