Data Analysis in Online Education: Tools and Techniques for Improving Academic Performance

The article explores the role of data analysis in improving student performance in online learning environments. With the expansion of distance learning during the COVID-19 pandemic, educational institutions faced growing challenges regarding increasing student engagement, predicting academic outcomes, and retaining students in school. These challenges remain relevant today. Data analysis techniques—from descriptive analyses to artificial intelligence-based predictive models—can help teachers and school administrations make informed decisions. Following a qualitative approach, through a literature review and the use of case studies, the research demonstrates how these tools can provide early intervention strategies for students at risk of falling behind and how they can create personalized learning experiences, analyzing key platforms such as Moodle and Canvas. The article also highlights the impact of third-party analytics integrations, which offer a more detailed understanding of student behavior and engagement. Although data analysis in online education has the potential to improve outcomes, there are challenges related to data interpretation, information overload, and institutional readiness to adopt these technologies. The study’s conclusions indicate that, by using the right tools and techniques, data analysis can lead to more adaptive and personalized learning environments, ultimately contributing to better academic performance.