TY - JOUR
T1 - Research Review on Big Data Usage for Learning Analytics and Educational Data Mining
T2 - 5th International Conference on Computing and Applied Informatics, ICCAI 2020
AU - Yunita, A.
AU - Santoso, H. B.
AU - Hasibuan, Z. A.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/6/23
Y1 - 2021/6/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85109048318&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1898/1/012044
DO - 10.1088/1742-6596/1898/1/012044
M3 - Conference article
AN - SCOPUS:85109048318
SN - 1742-6588
VL - 1898
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012044
Y2 - 1 December 2020 through 2 December 2020
ER -