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IPS146 Motoryn R. et al.
answers to test questions and to provide them with the necessary content for
the study and better assimilation of educational material in real time.
- 3V (variety): variability of processing algorithms for different types of
collected results; for example, homework results can be presented by gender,
age, health group, etc.
- 4V (veracity): high reliability of the data collected, allowing to formulate
representative results; for example, after conducting a national study of the
quality of education, it can be concluded that 4th year students have
significantly higher marks than 1st year students.
- 5V (value): the value of the accumulated data should be based on the
possibility of formulating useful multi-aspect dependences of the education
system on their basis. For example, it can be noted that when students move
from the first year to the second, the number of excellent and good students
decreases, however, there is an equal change in the proportion of assessments,
which may indicate a gradual complication of educational material; on the
other hand, the number of “C students”. Big data in education allow teachers
to receive a variety of information about the level of training students, the
assimilation of educational information, the oversight and Labs. Another
important problem of education is the identification of new, sometimes
hidden, relationships in big data, new knowledge. For this data mining
methods can be used to improve the organization of the educational process
and improve its management efficiency.
Another important area of research are issues related to internal
interaction. Predicting academic performance is one of the key themes of
research in the field of Big Data in education. Assessing academic achievement
is a difficult task, as student performance depends on various factors. The
relationship between performance parameters and factors involved to predict
performance in complex non-linear relationships, so the data collection areas
should be inclusive. Big data management allows processing of information
for the analysis of the key indicators of educational effectiveness. It is also
important to note the benefits of using big data for administrative staff of
higher education institutions. Academic performance, attendance,
scholarships and other personal information about students is subject to
continuous collection, processing and analysis. Working with this amount of
data requires considerable effort. Automation of the already routine work will
lead to savings of financial and human resources.
Methods of big data allow to form the relationship between types of
education and assess the progress and potential of the student throughout
his educational history. Such an approach can facilitate the formation of
individual educational itinerary taking into account the characteristics of each
student. There are five main types of data in the field of education:
- personal information;

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