How Should “Big Data” Change the Educational Process in Russian and Chinese Universities?

Abstract

The article discusses the potential of “big data” technology in optimizing the educational process of universities. It is noted that “big data” helps to gather a good evidence base for the dynamic analysis of student educational activities. It is noted that pedagogy is just beginning to develop the use of the “big data” capabilities and they are mainly related to the development of e-learning and the improvement of information and the educational environment. Particular attention is paid to the need for analytical support of online learning. The approaches to the application of “big data” at the level of educational organization and within the framework of a particular training course are highlighted. 1) The authors positively assess the experience of China in the use of big data to change the image of education. An opinion is expressed that Russian universities do not yet have the practice of using “big data” throughout the entire cycle of educational programs. The directions and methods of using big data in universities are proposed and discussed: integration of classroom and extracurricular network training, enhancement of the procedure for evaluating the educational activities of students. 2) “Big data” are useful for analyzing the university network platform, improving the efficiency of education quality management, and supporting remote informational and pedagogical interaction. It is defined that educational analytics involves the assessment of student educational achievements based on the analysis of metadata on participation in an electronic course and may include the assessment of “digital footprints” of students. It is argued that “big data” allows assessing the learning performance, predicting the expected results and directions of learning, and carrying out continuous in-depth monitoring of the educational process. At the same time, automated analysis significantly saves the lecturer’s working time and increases the reliability of assessment, so the assessment based on “big data” technology will become an indispensable function of the lecturer. Lecturers will be interested in mastering “big data” technology if they see other opportunities: for the supervision of external educational resources, fast processing of large arrays of scientific and educational information, and collection of reflexive information. So far, unfortunately, the massive educational information contained in electronic courses is not used by lecturers. Lecturers need to make full use of “big data” to establish the connection between student achievement and the curriculum, facilitate the development of teaching assignments, etc.