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IPS146 Motoryn R. et al.
5. Marketing will change: academic institutions will be able to learn in
advance about promising candidates;
6. More students will get to the end of education: now technologies
identify students at risk and help them;
7. Management of universities is optimized: institutions of different types
will be able to receive more accurate recommendations;
8. Lecturer’s will be able to help better lagging students: the programs
will let you know exactly which areas have problems;
9. It will be easier to choose a career: digital portfolios will tell your whole
story instead of you;
10. Data analysis will be a key element in the life of universities: using data
analysis at all levels, the administration will be able to make decisions that
are more effective.

References
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educational data mining // Paper presented at the Proceedings
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10.1109/FIE.2017.8190728.
2. Nasiri M., Minaei B., Vafaei F. (2012) Predicting GPA and academic
dismissal in LMS using educational data mining: A case mining //
Paper presented at the 3rd International Conference on eLearning
and eTeaching, ICeLeT. Р. 53-58. doi: 10.1109/ICELET.2012.6333365.
3. Mobasher G., Shawish A., Ibrahim O. (2017) Educational data mining
rule based recommender systems // Paper presented at the CSEDU 2017
- Proceedings of the 9th International Conference on Computer
Supported Education. № 1. - Р. 292-299.
4. V. Tam, E. Y. Lam, S. T. Fung, W. W. T. Fok, A. H. K. Yuen (2016) Enhancing
educational data mining techniques on online educational resources with
a semi-supervised learning approach // Paper presented at the
Proceedings of 2015 IEEE International Conference on Teaching,
Assessment and Learning for Engineering, TALE 2015. Р. 203-206. doi:
10.1109/TALE.2015.7386044.
5. Dwivedi S., Roshni V. S. K. (2017) Recommender system for big data in
education // Paper presented at the Proceedings - 5th National
Conference on E-Learning and E-Learning Technologies, ELELTECH. doi:
10.1109/ELELTECH.2017.8074993

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