Digital Footprint as a Source of Big Data in Education

The purpose of this study is to consider the prospects and problems of using big data in education.

Materials and methods. The research methods include analysis, systematization and structuring of information in the field of big data application in education, as well as modeling and software implementation of a test model for processing big data by using the Apache Spark framework.

Results. The article considers the key aspects of using big data in education. In particular, the sources of big data in the form of a digital footprint of learning, methods of analysis and areas of application of big data are considered. At the same time, the following sources of big data in education were identified: electronic educational environment and electronic library of the university; mobile applications for learning; university website; social networks and forums; feedback data, requests and surveys; personal data, including psychometric characteristics of students; data from scientific smart laboratories; data from video surveillance and access control systems; data on the career path and success of graduates. The use of big data in education includes the following points: personalization of e-learning, issuance of personalized recommendations; data analytics; assessment and feedback; predicting student success; monitoring the quality of education; creation of a learner model; development of curricula based on employer requests; development of new educational programs; emergence of new learning models; improvement of university management processes; improvement of the work of the admissions office; modernization of software and hardware teaching aids; optimization of the teaching staff. The following problems are considered as problems of using big data in education: protection of personal data, the need for new methodologies and technologies for analyzing big data, the need for significant modernization of the technical means available in the education system, the need for qualified personnel. The article also provides a test example of analyzing a log file (event log) of an e-course using Spark SQL big data processing technologies. The example shows the potential and practical applicability of big data processing technologies to the tasks of analyzing the digital footprint of learning.

Conclusion. Big data in education can provide unique opportunities for analyzing and optimizing the educational process, helping to identify trends, predict student success and adapt educational programs to the individual needs of students. However, the use of big data in the educational sphere also implies certain risks and challenges related to ethical aspects, protection of personal data and the need for personnel modernization of the existing education system. For the successful integration of data analytics into educational practice, it is necessary to develop not only technical resources, but also the level of digital security and ethics in the use of personal data.

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