This research aims to dive into Learning Analytics (LA) to help address the decreasing levels of reading comprehension. LA is a continuously evolving sector at the intersection of education, technology, and data science. It offers a robust understanding of the learning process, enabling personalized education and early intervention.
One area where learning analytics can make a significant impact is in reading comprehension, a fundamental skill for academic and personal success. This paper explores the application of learning analytics in predicting reading comprehension levels of students and extends valuable insights to schools, educators, and students.
The primary goal of this research is to answer the following research questions:
- Why does reading comprehension remain a persistent challenge?
- How can methodologies be applied to address this issue to suit individual students' reading comprehension needs?
- How can the application of data-driven approaches strengthen strategies for improving reading comprehension?
Learning Analytics can help create more effective interventions, tailored to the unique learning paths of students, and provide real-time feedback to educators. By understanding the challenges in reading comprehension, we aim to deliver actionable insights that improve learning outcomes.
This research contributes to:
- Personalized Education: Tailoring reading interventions to individual student needs.
- Early Intervention: Using data to identify students at risk of falling behind in reading comprehension.
- Data-Driven Insights: Leveraging analytics to inform better educational practices and strategies.