Abstract

The Advancement of technology has provided the opportunity to track and store students’ learning activities as big data sets within online environments. Big data refers to the capability of storing large quantities of data over an extended period and down to particular transactions. Educational data mining focuses on developing and implementing methods to promote discoveries from data in educational settings. It examines patterns in a large data set related to students’ actions. Learning analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, technology-learning enhancement, educational data mining, and visualization. LA increases awareness of learners and educators in their current situations that can help them make constructive decisions and more effectively perform their tasks. The literature review revealed that LA uses various methods including visual data analysis techniques, social network analysis, semantic, and educational data mining including prediction, clustering, relationship mining, discovery with models, and separation of data for human judgment to analyze data. Challenges include issues related to data tracking, collection, evaluation, analysis, lack of connection to learning sciences, optimizing learning environments, and ethical and privacy issues. Such a comprehensive overview provides an integrative report for faculty, course developers, and administrators about the methods, benefits, and challenges of LA so that they may apply LA more effectively to improve teaching and learning in higher education. This study has revealed the different methods used by learning analytics not only to show how the use of big data can benefit education but also to reveal the challenges faced by stakeholders in the educational process.