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IPS146 Motoryn R. et al.
The stage involved the collection and structuring of data submitted to
KNTEU, as well as the export of data collected in the form of 2-3NC.
4. Analysis of the data obtained the definition of ways to present the results,
the fixation of patterns. Comparison of the distribution of lecturers by age
compared with the normal distribution in the context of the subject taught.
Construction of "anomalous" distribution graphs.
5. Development of practical measures to regulate processes
After the adoption of the final report - transmission of negative trends to
the management bodies of
KNTEU, approval and implementation of an action plan for personnel policy
for 2018-2021.
Now are implemented of student performance indicators:
• monitoring current academic progress and identification of deviations (got
a bad mark; got a bad mark after an illness; got a bad mark, and the whole
group got good marks);
• identifying features of training in the university (favourite subjects that work
well, which does not skip);
• identifying what types of activity are good and bad (written work, the
answer is at the blackboard);
• building a circle of interests, based on visits to classes in subjects.

4. Discussion and Conclusion
Methods of big data allows forming the relationship between types of
education and assessing the progress and potential of the student
throughout his educational history. Such an approach can facilitate the
formation of an individual educational route, taking into account the
characteristics of each student.
The site OnlineUniversities identified ten areas in which higher
education will change under the influence of big data.
1. The method of working in groups will change: for example, at one of the
courses at Harvard, students with different answers are paired so that
they can come to a single decision, defending their point of view;
2. The learning experience will become more personal: technology allows
to individually selecting not only courses, but also homework and
careers;
3. Students will receive more recommendations: now the programs are
able to predict how well the course will be completed, even before it
has begun;
4. Data will play an important role in choosing a university: it assumed that
applicants would not even have to submit applications, because the
robots will select the best places for them;

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