Evaluate Curricula Balance for Software Engineering Education with using UGVA Method

DOI: https://doi.org/10.25559/SITITO.17.202103.684-696

Abstract

In these article deals with a methodological approach to comparing, evaluating and updating curricula in software engineering and computing. We describe the results of an analysis of seventy-three curricula for the Russian academic major «Informatics and Computing». For data concentration and visualization, we used the Unified Graphic Visualization of Activity (UGVA) method. We propose a model of balancing the learning load in the curricula. The images created using the described method allowed us to summarize curricula evaluation data, identify the differences in teaching students and best practices. In particular, it was found that most of the curricula have an insufficient number of hours devoted to courses for general and fundamental information theory and the cooperation with regional employers is low. Based on a comparison of the selected curriculum with others presented in the form of images in UGVA notation, we developed recommendations on changing the curriculum structure regarding courses developing key professional skills.

Author Biography

Viktor Alexandrovich Uglev, Siberian Federal University

Leading Researcher, Associate Professor of the Department of the Applied Physics and Space Technologies, Branch of Siberian Federal University, Cand. Sci. (Tech.)

References

1. Clear A., Clear T., Vichare A., Charles T., Frezza S., Gutica M., Lunt B., Maiorana F., Pears A., Pitt F., Reidesel C., Szynkiewicz J. Developing Competency Statements for Computer Science Curricula: The Way Ahead. Proceedings of the 2020ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, New York, USA; 2020. p. 515-516. (In Eng.) DOI: https://doi.org/10.1145/3341525.3394995
2. Ajanovski V. Evolutionary Curriculum Reconstruction: Process Model and Information System Development. Proceedings of the 18th Annual Conference on Information Technology Education. Association for Computing Machinery, New York, NY, USA; 2017. p. 89-94. (In Eng.) DOI: https://doi.org/10.1145/3125659.3125698
3. Tosic P., Beeston J. Designing Undergraduate Data Science Curricula: A Computer Science Perspective. 2018 ASEE Annual Conference & Exposition. ASEE Conferences, Salt Lake City, Utah; 2018. p. 1-11. (In Eng.) DOI: https://doi.org/10.18260/1-2--30283
4. Aldowah H., Al-Samarraie H., Fauzy W. Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics. 2019; 37:13-49. (In Eng.) DOI: https://doi.org/10.1016/j.tele.2019.01.007
5. Zykina A.V., Kaneva O.N., Munko V.V. The development of approaches for obtaining automated solution on the formation of the curriculum. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2018; 14(4):931-937. (In Eng.) DOI: https://doi.org/10.25559/SITITO.14.201804.931-937
6. Takada S., Cuadros-Vargas E., Impagliazzo J., Gordon S., Marshall L., Topi H., Veer G., Waguespack l. Toward the visual understanding of computing curricula. Education and Information Technologies. 2020; 25(5):4231-4270. (In Eng.) DOI: https://doi.org/10.1007/s10639-020-10127-1
7. Waguespack L., Topi H., Frezza S., Babb J., Marshall L., Takada S., van der Veer G., Pears A. Adopting Competency Mindful of Professionalism in Baccalaureate Computing Curricula. In: Ed. by J. Sharp. Proceedings of the EDSIG Conference on Information Systems and Computing Education. ISCAP, Cleveland, Ohio; 2019. Article number: n4955. 17 p. Available at: http://proc.iscap.info/2019/pdf/4955.pdf (accessed 17.08.2021). (In Eng.)
8. Crawley E.F., Malmqvist J., Östlund S., Brodeur D.R., Edström K. Rethinking Engineering Education. The CDIO approach. Springer, Cham; 2014. 311 p. (In Eng.) DOI: https://doi.org/10.1007/978-3-319-05561-9
9. Qi W. Learning Cell: Intelligent Technologies and Resources in Curriculum Development. In: Ed. by S. Yu, M. Ally, A. Tsinakos. Emerging Technologies and Pedagogies in the Curriculum. Bridging Human and Machine: Future Education with Intelligence. Springer, Singapore; 2020. p. 199-214. (In Eng.) DOI: https://doi.org/10.1007/978-981-15-0618-5_12
10. Sozykin A., Koshelev A., Bersenev A., Shadrin D., Aksenov A. Kuklin E. Developing Educational Programs Using Russian IT Job Market Analysis. 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE Press, Yekaterinburg, Russia; 2021. p. 0391-0394. (In Eng.) DOI: https://doi.org/10.1109/USBEREIT51232.2021.9454998
11. Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Third edition. Morgan Kaufmann, Elsevier; 2011. 560 p. (In Eng.) DOI: https://doi.org/10.1016/C2009-0-61819-5
12. Gorban A., Zinovyev A. Fast and user-friendly non-linear principal manifold learning by method of elastic maps. IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE Press, Paris, France; 2015. p. 1-9. (In Eng.) DOI: https://doi.org/10.1109/DSAA.2015.7344818
13. Chernoff H. The Use of Faces to Represent Points in K-Dimensional Space Graphically. Journal of the American Statistical Association. 1973; 68(342):361-368. (In Eng.) DOI: https://doi.org/10.1080/01621459.1973.10482434
14. Flury B., Riedwyl H. Graphical Representation of Multivariate Data by Means of Asymmetrical Faces. Journal of the American Statistical Association. 1981; 76(376):757-765. (In Eng.) DOI: https://doi.org/10.1080/01621459.1981.10477718
15. Hauthal E., Burghardt D. Retrieving, analyzing and visualizing emotion-related spatial information from user-generated content. KN ‒ Journal of Cartography and Geographic Information. 2015; 65(4):216-229. (In Eng.) DOI: https://doi.org/10.1007/BF03545144
16. Colasanti R., Borgo R., Jones M. Emoji and Chernoff – a Fine Balancing Act or are we Biased? 2019 IEEE Pacific Visualization Symposium (PacificVis). IEEE Press, Bangkok, Thailand; 2019. p. 102-111. (In Eng.) DOI: https://doi.org/10.1109/PacificVis.2019.00020
17. Cene E., Parim C., Özkan B. Comparing the performance of basketball players with decision trees and TOPSIS. Data Science and Applications. 2018; 1(1):21-28. Available at: http://jdatasci.com/index.php/jdatasci/article/view/3 (accessed 17.08.2021). (In Eng.)
18. Padilla L.M., Creem-Regehr S.H., Hegarty M., Stefanucci J.K. Decision making with visualizations: a cognitive framework across disciplines. Cognitive Research: Principles and Implications. 2018; 3:29. (In Eng.) DOI: https://doi.org/10.1186/s41235-018-0120-9
19. Sucontphunt T., Neumann U. 3D Facial Surface and Texture Synthesis Using 2D Landmarks from a Single Face Sketch. 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission. IEEE Press, Zurich, Switzerland; 2012. p. 152-159. (In Eng.) DOI: https://doi.org/10.1109/3DIMPVT.2012.65
20. Kukharev G.A., Shchegoleva N.L. Algorithms of Two-Dimensional Projection of Digital Images in Eigensubspace: History of Development, Implementation and Application. Pattern Recognition and Image Analysis volume. 2018; 28(2):185-206. (In Eng.) DOI: https://doi.org/10.1134/S1054661818020116
21. Kukharev G., Kaziyeva N. Digital Facial Anthropometry: Application and Implementation. Pattern Recognition and Image Analysis. 2020; 30(3):496-511. (In Eng.) DOI: https://doi.org/10.1134/S1054661820030141
22. Gupta S., Markey M.K., Bovik A.C. Anthropometric 3D face recognition. International Journal of Computer Vision. 2010; 90(3):331-349. (In Eng.) DOI: https://doi.org/10.1007/s11263-010-0360-8
23. Kossiakoff A., Sweet W.N., Seymour S.J., Biemer S.M. Systems Engineering Principles and Practice. 2nd ed. John Wiley & Sons, Inc.; 2011. 560 p. (In Eng.) DOI: https://doi.org/10.1002/9781118001028
24. Uglev V., Sychev O. Concentrating Competency Profile Data into Cognitive Map of Knowledge Diagnosis. In: Ed. by A. Basu, G. Stapleton, S. Linker, C. Legg, E. Manalo, P. Viana. Diagrammatic Representation and Inference. Diagrams 2021. Lecture Notes in Computer Science. 2021; 12909:443-446. Springer, Cham. (In Eng.) DOI: https://doi.org/10.1007/978-3-030-86062-2_46
25. Uglev V., Ustinov V. The New Competencies Development Level Expertise Method within Intelligent Automated Educational Systems. In: J. B. Perez, J. M. Corchado Rodríguez, et al. Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. Advances in Intelligent Systems and Computing. 2014; 293:157-164. Springer, Cham. (In Eng.) DOI: https://doi.org/10.1007/978-3-319-07476-4_19
Published
2021-09-30
How to Cite
UGLEV, Viktor Alexandrovich. Evaluate Curricula Balance for Software Engineering Education with using UGVA Method. Modern Information Technologies and IT-Education, [S.l.], v. 17, n. 3, p. 684-696, sep. 2021. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/777>. Date accessed: 18 sep. 2025. doi: https://doi.org/10.25559/SITITO.17.202103.684-696.
Citation Formats
Issue
Vol 17 No 3 (2021): Modern Information Technologies and IT-education
Section
IT education: methodology, methodological support
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing. 
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.