Utilizing Big Data Analytics Tools in E-learning Environments to Improve Personalized Learning Experience
Keywords:
big data analytics; e-learning environments; higher education; personalized learning experienceAbstract
Higher education often underestimates the value of data in decision-making, resulting in a lack of use of big data generated from e-learning (electronic learning) activities to improve the quality of online courses and redesign teaching and learning experiences. Big data play a pivotal role in improving the quality of content and resources, accessibility, automated assessment, and understanding the impact of e-learning courses on student engagement and participation. The aim of this study was to explore students’ engagement with online learning environments to improve learners’ personalized learning experience. The research tools used were the business analytics platform “Pyramid” as a tool for analyzing student interactions within e-learning environments, and the e-learning system “Blackboard” as a tool for collecting interaction data within the e-learning environment. The data were collected through learner engagement online in the learning management system of Blackboard. A descriptive analysis approach was used to analyze the data. A total of 20,000 students from a Saudi university undertook one of the main courses during the academic year 2022--2023. Using the convenience sampling method, which is a type of nonprobability sampling strategy that allows the selection of a study sample that can be easily reached during the course of the study, the study sample of 2,600 students was selected. The results revealed significant variations in interaction rates within the Blackboard e-learning environment, along with distinct fluctuations in the duration of learner engagement on the platform. Among the four daily time periods, the morning consistently emerged as the peak time for educational activity. Mobile application access in the morning demonstrated the highest level of engagement with the course elements and content. The interaction rates gradually declined throughout the day, reaching their lowest point in the afternoon. The results also revealed that students have a high level of academic achievement. Some learning strategies have been suggested to improve students’ participation rates during low-activity periods. This study highlights the importance of using learning analytics to provide exploratory insights into learner activity and develop data-driven strategies to meet individual learner needs and improve personalized learning experiences.
https://doi.org/10.26803/ijlter.24.9.38
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