Big Educational Data Analytics, Prediction and Recommendation: A Survey

Abstract

The development of mobile Internet, Internet of Things, and cloud computing has contributed to the unprecedented growth of information data. Big data plays a very important role in education. Currently, the literature review and in-depth research on big educational data are not very extensive, mainly involved in two fields: education mining and learning analysis. For a perfect research about education big data, this paper comprehensively reviewed three major aspects (Predictive Analytics, Learning Analytics, and Recommendation Systems) of educational data analytics for an intensive investigation and analysis: (1) Predictive Analytics: It predicts students’ learning performance by tracking students’ learning information and then analyzes students’ learning competence to build an academic early warning system; teachers can be allowed to intervene in students in time and adopts different teaching ways for different students. Therefore, both students’ learning and ability can be individualized and improved; (2) Learning Analytics: This part can identify the learners’ behavior patterns and obtain more implicit learner characteristics by studying the hidden meaning behind learning behaviors and strategies; (3) Recommendation Systems: It can match the needs of learners and recommend appropriate learning resources through different methods. All the above proved that the application of big data technology in education provides powerful data support for the development of education.