Methods of teaching educational data mining for pedagogical students

The aim of the article is to discuss and argue teaching educational data mining for pedagogical stu-dents and to describe the methodical system of educational data mining teaching for students with a middle level of mathematical and IT disciplines, that contributes to the development of student’s research competence. The relevance of the study is determined by the requirements for the ability of higher education graduates to analyze information and perform research using modern methods and technologies that are mentioned in the educational standards and the government order. They are associated with an increasing amount of accumulated data in various fields and the cost of the knowledge extracted from data.

Materials and methods. The article describes the author’s methodical system of educational data mining teaching, which was developed rely on: analysis of requirements and expectations to the re-search competence level, data analysis skills and modern education in general; comparison and analysis of the content of educational programs, books and courses on data mining and related dis-ciplines, generalization of pedagogical experience. The main aspects underlying the methodology: a form of flipped learning, a concentric (iterative) content structure, research teaching methods, a set of practical tasks for developing research competencies and Weka software for data mining as the main technical training tool for practical tasks implementation. The effectiveness of the developed methodological system was tested by the educational process monitoring, students questioning and statistical processing of questionnaires data.

Results. The study shows the relevance of educational data mining teaching for students of peda-gogical universities, studying in mathematical and informational specialization. The use of the de-scribed methodic system for senior pedagogical students allows increasing the level of research competence of students and significantly developing the competence of data analysis.

Conclusion. The described methodical system can used be partially or completely by teachers and methodologists for teaching data analysis at the modern level and development of research compe-tence of students with an average level of knowledge in mathematical and IT disciplines. 

1. Petrova M. V., Anufriyeva D. A. Study of the possibilities of data mining methods in modeling the educational process in the university. Vestnik CHuvashskogo universiteta = Bulletin of Chuvash University. 2013; 3: 280–285. (In Russ.)

2. Gal’chenko M.I., Gushchinskiy A.G. Data Mining and statistical methods in assessing the quality of refresher courses. Otkrytoye obrazovaniye = Open Education. 2014; 6: 15–22. (In Russ.)

3. SHamsutdinova T.M. Assessment of the quality of coursework: the competence approach. Obrazovaniye i nauka = Education and Science. 2018; 2 (20): 180–195. (In Russ.)

4. Akimov A. A. Designing an Information- Analytical Monitoring System Based on OLAP and Data Mining Technologies. Trudy Mezhdunarodnogo simpoziuma “Nadezhnost’ i kachestvo” = Proceedings of the International Symposium “Reliability and Quality”. 2011; 1: 235–239. (In Russ.)

5. Zorina N.V., Panchenko V.M. Experimental software for modeling and interpretation of educational data analysis processes. Sovremennyye informatsionnyye tekhnologii i IT-obrazovaniye = Modern information technologies and IT education. 2017; 4: 207–215. (In Russ.)

6. Gorlushkina N.N., Kotsyuba I.YU., KHlopotov M.V. Objectives and methods of intellectual analysis of educational data to support decision-making. Obrazovatel’nyye tekhnologii i obshchestvo = Educational technologies and society. 2015; 1: 472–482. (In Russ.)

7. Bakhshinategh B., Zaiane O., Elatia S., Ipperciel D. Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies. 2018; 23; .1; 537–553. DOI: https://doi.org/10.1007/s10639-017-9616-z

8. Romero C., Ventura: Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems Man and Cybernetics. Part C (Applications and Reviews). 2010. V. 40 (6; 601–618. DOI: https://doi.org/10.1109/TSMCC.2010.2053532

9. Belonozhko P.P., Karpenko A.P., KHramov D.A. Analysis of educational data: directions and prospects of application [Internet]. Internet-zhurnal «Naukovedeniye» = Internet magazine «Science». 2017; 9 (4). URL: http://naukovedenie.ru/PDF/15TVN417.pdf (Cited: 18.01.2019) (In Russ.)

10. Veryayev A.A., Tatarnikova G.V. Educational Data Mining and Learning Analytics - directions for the development of educational qualitology. Prepodavatel’ KHKHI vek = Teacher of the XXI century. 2016; 2: 150–160. (In Russ.)

11. Abrukov V.S., Efremov L.G., Koshcheyev I.G. . Opportunities for creating a decision support system and university management using the Deductor analytical platform. Integratsiya obrazovaniya = Education Integration. 2013; 1 (70): 17–23. (In Russ.)

12. Piotrovskaya K.R., Terbusheva E.A. Intellectual data analysis in pedagogical analytics. Tekhnicheskoye tvorchestvo molodezhi = Technical creativity of young people. 2016; 2 (96): 10–14. (In Russ.)

13. Rizayev I.S., YAkhina Z.T., Miftakhutdinov D.I. Computer technologies of data mining methods of data processing. Obrazovatel’nyye tekhnologii i obshchestvo = Educational technologies and society. 2015; 2: 514–526. (In Russ.)

14. Nesterov: A. About training in data mining technologies in the course «Corporate database systems». Prepodavaniye informatsionnykh tekhnologiy v Rossiyskoy Federatsii. Materialy XIV otkrytoy Vserossiyskoy konferentsii = Teaching information technologies in the Russian Federation. Proceedings of the XIV Open All-Russian Conference (St. Petersburg, May 19–20; 2016) Saint Petersburg: SPbSU; 2016: 78–79. (In Russ.)

15. Bodryakov V.YU., Bykov A.A. Methodical approaches to teaching students of the direction “Applied Mathematics and Computer Science” to the basics of intellectual processing of Big Data. Pedagogicheskoye obrazovaniye v Rossii = Pedagogical education in Russia. 2016; 7: 145–152. (In Russ.)

16. Kuznetsov O.A. The course “Machine learning” in the preparation of teachers of mathematics and computer science. Prepodavaniye informatsionnykh tekhnologiy v Rossiyskoy Federatsii. Materialy XVI otkrytoy Vserossiyskoy konferentsii = Teaching information technologies in the Russian Federation. Proceedings of the XVI Open All-Russian Conference (May 14–15; 2018, Moscow) Moscow: N.E. Bauman MSTU; 2018: 55–57. (In Russ.)

17. Terbusheva E.A. Development of research competence of future teachers for effective work in a high-tech educational environment. Novyye obrazovatel’nyye strategii v sovremennom informatsionnom prostranstve. Sbornik nauchnykh statey po materialam mezhdunarodnoy nauchnoy konferentsii = New educational strategies in the modern information space. Collection of scientific articles on the materials of the international scientific conference (March 12-26; 2018, St. Petersburg) Saint Petersburg: A. I. Gertsen RSPU; 2018: 178- 182. (In Russ.)

18. Piotrovskaya K.R., Terbusheva E.A. Intellectual data analysis and development of research skills of bachelors [Internet]. Sovremennyye problemy nauki i obrazovaniya. = Modern problems of science and education. 2017; 1. URL: http://science-education.ru/ru/article/view?id=26084 (Cited: 08.06.2019) (In Russ.)

19. Eibe Frank, Mark A. Hall, Ian H. Witten. The WEKA Workbench. Online Appendix for «Data Mining: Practical Machine Learning Tools and Techniques». 4th Ed. Burlington: Morgan Kaufmann. 2016.

20. Hajizadeh N., Ahmadzadeh M., Analysis of factors that affect students’ academic performance - Data Mining Approach [Internet]. International Journal of advanced studies in Computer Science and Engineering. 2014. V. 3. I. 8. URL: http://www.ijascse.org/volume-3-issue-8/Data_mining_approach.pdf

21. Sunita B. Aher, Louis Lobo. Applicability of data mining algorithms for recommendation system in e-learning. Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI ‘12). New York: ACM; 2012; 1034-1040.

22. Brenda Reyes Ayala, Jiangping Chen. A machine learning approach to evaluating translation quality. Proceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries (JCDL ‘17). Piscataway: IEEE Press; 2017. 281-282.

23. Tarek Elghazaly, Amal Mahmoud, and Hesham A. Hefny. Political Sentiment Analysis Using Twitter Data. Proceedings of the International Conference on Internet of things and Cloud Computing (ICC ‘16). New York: ACM; 2016. 5 p.

24. Podlasyy I.P. Pedagogika = Pedagogy. Book 2. 2nd ed. Moscow: Vlados; 2007. 575 p. (In Russ.)