Technology of Multilevel Interuniversity Indicators as a Factor for Increasing Academic Mobility. Experience Based on Russian Federal Educational Standards

Мұқаба


Дәйексөз келтіру

Толық мәтін

Аннотация

Introduction. At the present time, more and more students are changing either their field of study or the university in the process of studying. This raises the problem of how to determine whether a student’s level of knowledge meets the host institution’s criteria. A simple comparison of competencies is not enough. Therefore, the authors propose a new system of comparing existing and required knowledge (competencies) at the new place of study. The purpose of this article is to present the results of research on the development and practical application of specific “competency trees” that allow for the automatic comparison and re-crediting of disciplines.

Materials and Methods. The research is based on the methods of system analysis for weakly formalized problems: the method of expert evaluations and the method of the goal tree. For direct development the method of construction of binary decision trees was used. To evaluate the effectiveness of the developed method, methods of observation and comparison were used.

Results. This article describes the specific steps for creating checklists based on multilevel competency indicator trees. The tables describe the four levels of competency acquisition. Based on the experiments carried out on the use of such tables for retake disciplines when transferring a student from one specialty to another, the following recommendations are made: if it is necessary to obtain a mark of the “Test” type in the Host University, the comparison is made according to the second level indicators; if it is necessary to obtain a mark of the type “Graded test/Test with a grade” in the Host University, the comparison is made according to the third level indicators; if it is necessary to obtain a mark of the “Exam” type in the Host University, the comparison is made according to the indicators of the deepest level for this indicator of the first level. The technique has been successfully tested for moving of a student within Kazan National Research Technical University named after A. N. Tupolev-KAI between the academic programs Aircraft Engineering and Applied Mathematics and Informatics.

Discussion and Conclusion. The proposed multilevel system of interuniversity indicators will significantly simplify the procedure for transferring subjects for students who are moved from one study program to another at any level – whether within one university, or between different universities of the Russian Federation. The use of an automated system for comparing the level of knowledge of a student when moving from one university to another will not only reduce the time of a student and teachers, but also eliminate the human factor, bias and subjectivity in the process of making decisions about transferring, and increase the transparency of this process. All this together will contribute to the development of academic mobility of students, increasing their competitiveness in the labor market and strengthening academic interuniversity relationships both in Russia and abroad.

Авторлар туралы

Alexander Snegurenko

Kazan National Research Technical University named after A. N. Tupolev-KAI

Email: APSnegurenko@kai.ru
ORCID iD: 0000-0002-5515-3544
Scopus Author ID: 57208723750
ResearcherId: I-1028-2017

Associate Professor of the Chair of Industrial Economics and Management,  Ph.D. (Eng.)

Ресей, Kazan

Sergey Zaydullin

Kazan National Research Technical University named after A. N. Tupolev-KAI

Email: SSZaydullin@kai.ru
ORCID iD: 0000-0002-8285-9817

Head of the Chair of Applied Mathematics and Informatics, Ph.D. (Eng.), Associate Professor

Ресей, Kazan

Svetlana Novikova

Kazan National Research Technical University named after A. N. Tupolev-KAI; National Research Mordovia State University

Хат алмасуға жауапты Автор.
Email: SVNovikova@kai.ru
ORCID iD: 0000-0001-8207-1010
Scopus Author ID: 57203542635
ResearcherId: В-6505-2017

Professor of the Chair of Applied Mathematics and Informatics; Professor of the Chair of Applied Mathematics, Differential Equations and Theoretical Mechanics, Dr.Sci. (Eng.)

Ресей, Kazan; Saransk

Natalia Valitova

Kazan National Research Technical University named after A. N. Tupolev-KAI

Email: NLValitova@kai.ru
ORCID iD: 0000-0002-8408-1885
Scopus Author ID: 23013218200

Associate Professor of the Chair of Applied Mathematics and Informatics, Ph.D. (Eng.)

Ресей, Kazan

Elmira Kremleva

Kazan National Research Technical University named after A. N. Tupolev-KAI

Email: EShKremleva@kai.ru
ORCID iD: 0000-0003-0858-0575
Scopus Author ID: 57194618280

Senior Lecturer of the Chair of Applied Mathematics and Informatics

Ресей, Kazan

Әдебиет тізімі

  1. Lauder H., Mayhew K. Higher Education and the Labour Market: An Introduction. Oxford Review of Education. 2020; 46(1):1-9. (In Eng.) doi: https://doi.org/10.1080/03054985.2019.1699714
  2. Romashov R.A., Lipinsky D.A., Musatkina A.A., Rakova E.G., Revina S.N. Bologna Process as a Factor of Integration of Educational Systems of Russia and the West. Laplage em Revista (International). 2021; 7(3D):35-42. (In Eng.) doi: https://doi.org/10.24115/S2446-6220202173D1688p.35-42
  3. Kwiek M. The Emergent European Educational Policies under Scrutiny: the Bologna Process from a Central European Perspective. European Educational Research Journal. 2004; 3(4):759-776. (In Eng.) doi: http:// dx.doi.org/10.2304/eerj.2004.3.4.3
  4. Krücken G., Mishra S., Seidenschnur T. Theories and Methods in Higher Education Research – a Space of Opportunities. European Journal of Higher Education. 2021; 11(1):461-467. (In Eng.) doi: https://doi.org/10.1080/21568235.2021.2004905
  5. Grebnev L. The Quality of Teaching in Different Higher Educations. Higher Education in Russia and Beyond. 2021; (4):8-10. Available at: https://herb.hse.ru/data/2021/11/30/1451210582/1HERB_29_print%20(1).pdf#page=8 (accessed 01.08.2021). (In Eng.)
  6. López-Duarte C., Maley J., Vidal-Suárez M. Main Challenges to International Student Mobility in the European Arena. Scientometrics. 2021; 126:8957-8980. (In Eng.) doi: http://doi.org/10.1007/s11192-021-04155-y
  7. Dekhnich O.V., Lyutova O.V., Trubitsyn M.A., Danilova E.S. More International Students Coming to Russia: Pros and Cons. Integratsiya obrazovaniya = Integration of Education. 2021; 25(2):244-256. (In Russ., abstract in Eng.) doi: https://doi.org/10.15507/1991-9468.103.025.202102.244-256
  8. Prakhova M.U., Svetlakova S.V., Zaichenko N. V., Khoroshavina E. A., Krasnov A.N. The Conception of Point-Rating System for Assessment of Students’ Educational Results. Vysshee obrazovanie v Rossii = Higher Education in Russia. 2016; (3):17-25. https://vovr.elpub.ru/jour/article/view/486/436 (accessed 01.08.2021). (In Russ., abstract in Eng.)
  9. Dvulichanskaya N., Ermolaeva V. The Role of Point-Rating System of Learning Outcomes Assessment in Implementing the Federal State Educational Standard of Higher Professional Education. Standarty i monitoring v obrazovanii = Standards and Monitoring in Education. 2015; 3(4):3-7. (In Russ., abstract in Eng.) doi: https://doi.org/10.12737/12923
  10. Syromyasov A.O. Application of the System of Assessment based on Points at a Institution of Higher Education. Integratsiya obrazovaniya = Integration of Education. 2013; (2):15-21. Available at: http://edumag.mrsu.ru/content/pdf/13-2.pdf (accessed 01.08.2021). (In Russ., abstract in Eng.)
  11. Gugina E., Kuzenkov O. Educational Standards of the Lobachevsky State University of Nizhni Novgorod. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo = Vestnik of Lobachevsky University of Nizhni Novgorod. 2014; (3):39-44. Available at: http://www.unn.ru/pages/e-library/vestnik/19931778_2014_-_3-4_unicode/7.pdf (accessed 01.08.2021). (In Russ., abstract in Eng.)
  12. Velichová D., Gustafsson T. Special issue: Contributions from the SEFI Working Group on Mathematics Conference 2016. Teaching Mathematics and its Applications: An International Journal of the IMA. 2017; 36(2):65-66. (In Eng.) doi: https://doi.org/10.1093/teamat/hrx010
  13. Kuzenkov O.A., Zakharova I.V. Mathematical Programs Modernization Based on Russian and International Standards. Modern Information Technologies and IT-Education. 2018; 14(1):233-244. (In Eng.) doi: https://doi.org/10.25559/SITITO.14.201801.233-244
  14. Guerrero D., De los Ríos I. Professional Competences: a Classification of International Models. Procedia – Social and Behavioral Sciences. 2012; 46:1290-1296. (In Eng.) doi: https://doi.org/10.1016/j.sbspro.2012.05.290
  15. Dall’alba G., Sandberg J. Educating for Competence in Professional Practice. Instructional Science. 1996; 24:411-437. (In Eng.) doi: https://doi.org/10.1007/BF00125578
  16. Lebedeva T., Okhotnikova N., Potapova E. Electronic Educational Environment of High School: The Requirements, Capabilities, Experience and Perspectives of Application. Mir nauki. Pedagogika i psikhologiya = World of Science. Pedagogy and Psychology. 2016; 4(2). Available at: https://mir-nauki.com/PDF/57PDMN216.pdf (accessed 01.08.2021). (In Russ., abstract in Eng.)
  17. Medvedeva O.N., Suponev N.P., Soldatenko I.S., Zakharova I.V., Yazenin A.V. [On the Electronic Educational Environment and the System for Assessing the Quality of Educational Activities at Tver State University]. Obrazovatelnye tekhnologii i obshchestvo = Educational Technology & Society. 2014; 17(4):610-624. Available at: https://readera.org/ob-jelektronnoj-obrazovatelnoj-srede-i-sisteme-ocenki-kachestva-obrazovatelnoj-14062576 (accessed 01.08.2021). (In Russ.)
  18. Kraleva R.S., Kralev V.S., Sinyagina N., Koprinkova-Hristova P., Bocheva N. Design and Analysis of a Relational Database for Behavioral Experiments Data Processing. International Journal of Online and Biomedical Engineering. 2018; 14(02):117-132. (In Eng.) doi: https://doi.org/10.3991/ijoe.v14i02.7988
  19. Dangerfield B.J., Morris J.S. Relational Database Management Systems: A New Tool for Coding and Classification. International Journal of Operations & Production Management. 1991; 11(5):47-56. (In Eng.) doi: https://doi.org/10.1108/01443579110143449
  20. Oka H., Yoshizawa A., Shindo H., Matsumoto Y., Ishii M. Machine Extraction of Polymer Data from Tables Using XML Versions of Scientific Articles. Science and Technology of Advanced Materials: Methods. 2021; 1(1):12-23. (In Eng.) doi: https://doi.org/10.1080/27660400.2021.1899456
  21. Murray-Rust P., Rzepa H.S. Scientific Publications in XML – Towards a Global Knowledge Base. Data Science Journal. 2006; 1(1):84-98. Available at: https://datascience.codata.org/articles/abstract/182 (accessed 01.08.2021). (In Eng.)
  22. Grune D., Jacobs C.J.H. Parsing as Intersection. In: Grune D., Jacobs C.J.H. (eds.) Monographs in Computer Science. New York: Springer; 2008. p. 425-442 (In Eng.) doi: https://doi.org/10.1007/978-0-387-68954-8_13
  23. Sun Z., Wang R., Luo Z. Polynomial Approximation Based Spectral Dual Graph Convolution for Scene Parsing and Segmentation. Neurocomputing. 2021; 438:133-144. (In Eng.) doi: https://doi.org/10.1016/j.neucom.2021.01.002
  24. Leroy G., Chen H., Martinez J.D. A Shallow Parser Based on Closed-Class Words to Capture Relations in Biomedical Text. Journal of Biomedical Informatics. 2003; 36(3):145-158. (In Eng.) doi: https://doi.org/10.1016/S1532-0464(03)00039-X
  25. Bedny A., Erushkina L., Kuzenkov O., Modernising Educational Programmes in ICT Based on the Tuning Methodology. Tuning Journal for Higher Education. 2014; 1(2):387-404. (In Eng.) doi: https://doi.org/10.18543/tjhe-1(2)-2014pp387-404
  26. Haas C., Hadjar A. Students’ Trajectories Through Higher Education: A Review of Quantitative Research. Higher Education. 2020; 79:1099-1118. (In Eng.) doi: https://doi.org/10.1007/s10734-019-00458-5
  27. Shulruf B., Tumen S., Hattie J. Student Pathways in a New Zealand Polytechnic: Key Factors for Completion. International Journal of Vocational and Technical Education. 2010; 1(4):67-74. Available at: https://citeseerx. ist.psu.edu/viewdoc/download?doi=10.1.1.1008.4433&rep=rep1&type=pdf (accessed 01.08.2021). (In Eng.)

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