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Integrating Sentiment Analysis with Learning Analytics for Improved Student Engagement
Abstract
In the growing field of e-learning, understanding student engagement is critical for enhancing the learning experience and improving outcomes. This paper presents an innovative approach by integrating Sentiment Analysis (SA) with Learning Analytics (LA) to better gauge and improves student engagement. By analyzing textual interactions from discussion forums, assignments, and feedback, the system detects student emotions and attitudes toward course content. These insights are combined with traditional learning analytics data—such as click rates, time spent on activities, and quiz results—to provide a more comprehensive understanding of student engagement. A sentiment-aware model was developed and evaluated across various e-learning platforms, showing improved accuracy in predicting student performance and engagement, along with more targeted interventions by instructors. Results demonstrate that this integration can lead to a 20% increase in engagement and a 15% improvement in overall academic performance.
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