Development of an Algorithm of the Formation of IT Project Teams Based on Data from the Digital Footprint of Students
Antonina Vladimirovna MELNIKOVA,
Marina Sergeevna VOROBEVA,
Elizaveta Vladimirovna EGOROVA,
Elizaveta Dmitrievna CHEKANOVA https://doi.org/10.15514/ISPRAS-2024-36(3)-15
Abstract
The article discusses the development of an algorithm for the formation of IT project teams. The materials were data from the digital footprint of IT students. The student's digital footprint is a constantly updated set of data, including accounting documents of project disciplines, intermediate results in disciplines, and practical training. The paper provides an example of solving the problem of forming teams using graphs. An algorithm based on a graph model has been proposed, which allows you to build a graph reflecting the interaction of students in past projects. Commands are formed based on the constructed graph. Two approaches to command formation are proposed inside the graph model: based on vertex clustering and using graph traversal. To determine the best team, a graph of student communication is built with text tags representing technologies, programming languages, frameworks, etc. The algorithm was tested on data from students of the IT department of Mathematical Support and Administration of Information Systems of the School of Computer Science and requirements for a real project and tested on the spontaneous distribution of students on projects within the discipline. Using the algorithm, you can estimate how successful the split was. The creation of effective teams plays a key role in the successful implementation of projects, therefore, the proposed algorithm can be useful for teachers and project managers in the IT field. The developed algorithm is planned to be integrated into the IT project executors search web service.
About the Authors
Antonina Vladimirovna MELNIKOVA
University of Tyumen
Russian Federation
Russian Federation
Assistant at the Software Department of University of Tyumen, a postgraduate student. Research interests: natural language processing, data analysis, programming languages.
Marina Sergeevna VOROBEVA
University of Tyumen
Russian Federation
Russian Federation
Cand. Sci. (Tech.), Associate Professor, Head of the Software Department of University of Tyumen. Research interests: construction of mathematical and information models, research methods and machine learning technologies, methods for students’ digital footprint analysis.
Elizaveta Vladimirovna EGOROVA
University of Tyumen
Russian Federation
Russian Federation
Bachelor's student in the field of Mathematical Support and Administration of Information Systems at University of Tyumen. Research interests: data analysis, machine learning, natural language processing, deep learning, big data, generative models.
Elizaveta Dmitrievna CHEKANOVA
University of Tyumen
Russian Federation
Russian Federation
Bachelor's student in the field of Mathematical Support and Administration of Information Systems at University of Tyumen. Research interests: programming languages, programming technologies, machine learning, data analysis.
References
1. Асанов А. З., Мышкина И. Ю. Процедура формирования команды исполнителей проекта на основе когнитивных карт и генетических алгоритмов /А. З. Асанов, И. Ю. Мышкина // Проблемы управления и моделирования в сложных системах: Труды XXI Международной конференции. В 2-х томах, Самара, 03-06 сентября 2019 года / Под редакцией С.А. Никитова, Д.Е. Быкова, С.Ю. Боровика, Ю.Э. Плешивцевой, Том II. – Самара: Общество с ограниченной ответственностью "Офорт", 2019. – С. 354-358. / Asanov A.Z., Myshkina I.Yu. The procedure for forming a team of project performers based on cognitive maps and genetic algorithms. Problems of control and modeling in complex systems: Proceedings of the XXI International Conference. Samara: Ofort, 2019. P. 354-358. (in Russian) Доступно по ссылке: https://elibrary.ru/item.asp?id=41101033, 01.10.2023.
2. Захарова И.Г., Боганюк Ю.В., Воробьева М.С., Павлова Е.А. Диагностика профессиональной компетентности студентов ИТ-направлений на основе данных цифрового следа // Информатика и образование. 2020. № 4. – С. 4-11. / Zakharova I.G., Boganyuk Yu.V., Vorobyova M.S., Pavlova E.A. Diafnostics of profecssional competence of IT students based on digital footprint data. Informatics and Education, 2020, no. 4, p. 4-11. (in Russian) https://doi.org/10.32517/0234-0453-2020-35-4-4-11.
3. Захарова И.Г. Методы машинного обучения для информационного обеспечения управления профессиональным развитием студентов. Образование и наука. 2018;20(9):91-114. / Zakharova I.G. Machine Learning Methods of Providing Informational Management Support for Students’ Professional Development. The Education and science journal. 2018;20(9):91-114. (in Russian) https://doi.org/10.17853/1994-5639-2018-9-91-114.
4. Решение TEAM AS A SERVICE – Команда как сервис – Текст: электронный // Effective technologies: [сайт]. – 2022. Доступно по ссылке: https://www.effective-group.ru/solutions/TAAS.html, 04.10.2023.
5. The Open Source Community for Collaboration Solutions – Текст: электронный // openntf: [сайт]. – 2021. Доступно по ссылке: https://www.openntf.org/main.nsf, 04.10.2023.
6. Radin Hamidi Rad, Aabid Mitha. PyTFL: A Python-based Neural Team Formation Toolkit // Radin Hamidi Rad, Aabid Mitha, Hossein Fani, Mehdi Kargar, Jaroslaw Szlichta, Ebrahim Bagheri / In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM '21). Association for Computing Machinery 4716–4720. – 2021. https://doi.org/10.1145/3459637.3481992.
7. Воробьева М. С., Мельникова А. В., Захаров С. В. Формирование студенческих команд для IT-проектов на основе отчетных документов по практике / М. С. Воробьева, А. В. Мельникова, С. В. Захаров // Информатизация образования и методика электронного обучения : цифровые технологии в образовании : материалы VII Междунар. науч. конф. Красноярск, 19-22 сентября 2023 г. / под общ. ред. М.В. Носкова. – Красноярск: Красноярский государственный педагогический университет им. В.П. Афанасьева, 2023. – С. 1024-1028. / Vorobeva M.S., Melnikova A.V., Zakharov S.V. Formation of student team for IT-projects on the basis of reporting documents on practice. Informatization of education and e-learning methods: digital technologies in education: materials VII International Scientific Conference Krasnoyarsk, September 19-22, 2023, ed. by M.V. Noskov, Krasnoyarsk: Krasnoyarsk State Pedagogical University named after V.P. Afanasyev, 2023, p. 1024-1028. (in Russian) Доступно по ссылке: https://www.elibrary.ru/item.asp?id=54778516, 14.10.2023.
8. Асанов А.З., Мышкина И.Ю., Грудцына Л.Ю. Применение графовых моделей в проектном управлении // Онтология проектирования. 2023. Т.13, №2(48). С.232-242. / Asanov AZ, Myshkina IY, Grudtsyna LYu. Application of graph models in project management. Ontology of designing. 2023; 13(2): 232-242. (in Russian) DOI: 10.18287/2223-9537-2023-13-2-232-242.
9. Radin Hamidi Rad, Hossein Fani, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, and Jaroslaw Szlichta. 2023. A Variational Neural Architecture for Skill-based Team Formation. ACM Trans. Inf. Syst. 42, 1, Article 7 (January 2024), 28 pages. https://doi.org/10.1145/3589762.
10. Zhaoa X., Lianga J., Wangab J. A community detection algorithm based on graph compression for large-scale social networks / Xingwang Zhaoa, Jiye Lianga, Jie Wangab // Information Sciences, No 551. – 04.2021. – P. 358–372. https://doi.org/10.1016/j.ins.2020.10.057.
11. Гринева, Н. В. Сравнительный анализ спектральных методов выделения сообществ / Н. В. Гринева // Актуальные проблемы прикладной математики, информатики и механики: сборник трудов Международной научной конференции, Воронеж, 12–14 декабря 2022 года / Воронежский государственный университет. – Воронеж: Научно-исследовательские публикации, 2023. – С. 389-396. / Grineva, N. V. Comparative analysis of spectral methods of community allocation / N. V. Grineva // Actual problems of applied mathematics, computer science and mechanics: proceedings of the International Scientific Conference, Voronezh, December 12-14, 2022 / Voronezh State University. Voronezh: Scientific Research Publications, 2023. p. 389-396. (in Russian) Доступно по ссылке: https://www.elibrary.ru/item.asp?id=54251945, 14.10.2023.
12. Yangyang Guo, Hao Wang. A reinforcement learning-assisted genetic programming algorithm for team formation problem considering person-job matching / Yangyang Guo, Hao Wang, Lei He, Witold Pedrycz, P. N. Suganthan, Yanjie Song // arXiv preprint arXiv:2304.04022. – 2023.
13. Payumo Jane, He Guangming. Mapping Collaborations and Partnerships in SDG Research / Payumo Jane, He Guangming, Manjunatha Anusha Chintamani, Higgins Devin, Calvert Scout // Frontiers in Research Metrics and Analytics. – 2021. https://doi.org/10.3389/frma.2020.612442.
14. Liu M. et al. Team formation and team performance: The balance between team freshness and repeat collaboration / Meijun Liu, Ajay Jaiswal, Yi Bu, Chao Min, Sijie Yang, Zhibo Liu, Daniel Acuña, Ying Ding //Journal of Informetrics Volume 16 Issue 4, 2022. https://doi.org/10.1016/j.joi.2022.101337.
Review
For citations:
MELNIKOVA A.V., VOROBEVA M.S., EGOROVA E.V., CHEKANOVA E.D. Development of an Algorithm of the Formation of IT Project Teams Based on Data from the Digital Footprint of Students. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2024;36(3):213-224. (In Russ.) https://doi.org/10.15514/ISPRAS-2024-36(3)-15
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