Data Analytics-Based Learning Analytics to Evaluate the Effectiveness of Online Learning in Secondary Schools: A Systematic Literature Review

Abstract

Online learning has become a dominant mode of instruction in secondary education, particularly after the COVID-19 pandemic. However, evaluating its effectiveness remains a complex challenge due to the massive amount of digital learning data generated by students. This study aims to analyze how learning analytics based on data analytics are utilized to assess the effectiveness of online learning in secondary schools. This research employs a systematic literature review (SLR) method by analyzing 35 peer-reviewed journal articles published between 2019 and 2024, indexed in Scopus, Web of Science, and ERIC databases. The findings indicate that learning analytics plays a significant role in monitoring student engagement, learning behavior, academic performance, and learning outcomes. Most studies emphasize indicators such as login frequency, interaction intensity, time-on-task, and assessment scores as key parameters for measuring effectiveness. Furthermore, data analytics enables teachers and schools to make data-driven decisions in improving instructional strategies and student support systems. This study concludes that learning analytics is a powerful tool for enhancing the quality of online learning, although challenges related to data privacy, ethical issues, and teachers’ data literacy still need to be addressed.