Integrating Sentiment Analysis with Learning Analytics for Improved Student

The integration of sentiment analysis with learning analytics offers a novel approach to improving student outcomes by providing deeper insights into the emotional and cognitive states of learners. This research explores the use of sentiment analysis on student interactions, such as online discussions, assignments, and feedback, to assess the emotional tone of student engagement. By combining these sentiment insights with traditional learning analytics, which track academic progress and behavior patterns, this study aims to create a comprehensive model that enhances the identification of students at risk, tailor educational interventions, and fosters personalized learning experiences. The proposed approach not only improves the monitoring of student well-being and engagement but also supports the development of adaptive learning systems that respond to students’ emotional states. Results show that sentiment analysis integrated with learning analytics can provide real-time feedback for educators, enhancing student support and improving overall academic performance