Digitalization and the development of information technology, especially Artificial Intelligence, have been embraced in all fields. At the same time, data has grown mostly from the digital footprints or any technology information system. The development of technology and big data offers enormous opportunities to conduct big data analytics in any field, including education. This study aims to review current research related to big data analytics in education and explain future research direction. Using Kitchenham's technique, we selected and clustered the literature into the types of data, methods, type of data analytics and learning analytics application used. The results show that research of big data learning analytics generally aims to improve the learning process, analyze learner behaviour for student profiling, improve student retention and evaluate student feedback in the context of MOOCs and Learning Management System. Several future directions for this topic are: 1) building a big open dataset including data pre-processing and addressing the problem of imbalanced dataset, 2) process mining for learning log activity to gain knowledge and insights from online behaviour, not only from the perspective of the learner but also from the activities of the teacher, 3) designing an automated framework which uses big data and allows descriptive, predictive, prescriptive analytical learning to be carried out. To summarize, embracing big data to learning analytics and educational data mining is an open research area that seems very powerful in education.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 23 Jun 2021|
|Event||5th International Conference on Computing and Applied Informatics, ICCAI 2020 - Medan, Indonesia|
Duration: 1 Dec 2020 → 2 Dec 2020