infojournalИнформатика и образованиеInformatics and education0234-04532658-7769Publishing House Education and Informatics10.32517/0234-0453-2025-40-6-39-48infojournal-1364Research ArticleИНФОРМАТИЗАЦИЯ ОБРАЗОВАНИЯINFORMATIZATION OF EDUCATIONАвтоматизированная система оценки и выбора эффективной траектории обучения магистров в условиях изменчивости внешних факторов с учетом мнения индустриальных партнеров университетаComputer-aided system for assessing and selecting effective masters' learning trajectory in variability of external factors considering the university industrial partners' opinionhttps://orcid.org/0000-0003-4362-1221ВишнековА. В.VishnekovA. V.

Андрей Владленович Вишнеков, доктор тех. наук, профессор, профессор департамента

департамент компьютерной инженерии

Москва

Andrey V. Vishnekov, Doctor of Sciences (Engineering), Professor, Professor at School

HSE Tikhonov Moscow Institute of Electronics and Mathematics; School of Computer Engineering

Moscow

avishnekov@hse.ruhttps://orcid.org/0000-0002-0063-9758ИвановаЕ. М.IvanovaE. M.

Елена Михайловна Иванова,  канд. тех. наук, доцент, доцент департамента

департамент компьютерной инженерии

Москва

Elena M. Ivanova, Candidate of Sciences (Engineering), Docent, Associate Professor

HSE Tikhonov Moscow Institute of Electronics and Mathematics; School of Computer Engineering

Moscow

emivanova@hse.ruhttps://orcid.org/0009-0004-0556-7097ЖурсуноваН.ZhursunovaN.

Найля Журсунова, магистрант

департамент компьютерной инженерии

Москва

Zhursunova Nayla, master's student

HSE Tikhonov Moscow Institute of Electronics and Mathematics; School of Computer Engineering

Moscow

nzhursunova@edu.hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of EconomicsRussian Federation2025020120264063948Copyright © Вишнеков А.В., Иванова Е.М., Журсунова Н., 20262026Вишнеков А.В., Иванова Е.М., Журсунова Н.Vishnekov A.V., Ivanova E.M., Zhursunova N.This work is licensed under a Creative Commons Attribution 4.0 License.https://info.infojournal.ru/jour/article/view/1364

   Развитие современной системы образования характеризуется множеством неопределенных, динамично меняющихся факторов.

   Целью и новизной представленного в данной статье исследования является разработка автоматизированной системы построения эффективной образовательной траектории в условиях неопределенности внешних факторов.

   Разработанная система является инструментом оценки и динамической корректировки образовательного процесса в соответствии с текущими и возможными в будущем изменениями внешних факторов. Индустриальные партнеры университета получают реальную возможность непосредственного участия в процессах формирования набора критериев оценки и выбора образовательных траекторий. Разработанная автоматизированная система объединяет методы поддержки принятия решений, а именно метод аналитических иерархий, метод платежной матрицы для учета мнения экспертов и метод кластеризации k-means для анализа мнения студентов. В экспериментальном исследовании системы приняли участие более 100 магистрантов: 60 студентов 1-го курса и 46 студентов 2-го курса. Анализ показал, что применение разработанной системы поддержки принятия решений позволило увеличить посещаемость занятий на 24 % и академическую успеваемость студентов, при этом доля студентов, получающих отличные оценки, увеличивается на 9 %. Применение системы позволяет экспертам (администрации и преподавателям университетов, индустриальным партнерам образовательной программы) оперативно модернизировать образовательную траекторию. Лекторы могут заранее подготовиться к возможной реорганизации учебного процесса в случае изменения внешних условий.

   Новизна предложенного подхода заключается в интегрировании в единую систему управления образовательным процессом множества современных и классических методов: интервьюирования, кластеризации, поддержки принятия решений, прогнозирования изменения внешних факторов, построения образовательной траектории с учетом мнения всех участников образовательного процесса.

   The modern education system development is characterized by many uncertain, dynamically changing factors.

   The purpose and originality of the study presented in the article is to develop an automated system for building an effective educational trajectory in the conditions of external factors’ uncertainty.

   The developed system is a tool for assessment and dynamic adjustment of the educational process in accordance with the ongoing and possible future changes in external factors. The university industrial partners get a real opportunity to participate directly in the creation of a valuation criteria set and in the selection of educational trajectories. The developed automatized system integrates decision support methods, namely analytic hierarchy process, payoff matrix method, to consider the viewpoint of experts, and k-means clustering method for analyzing the students’ opinion. The experimental study of the system involved more than 100 master’s students: sixty 1st year students and forty six 2nd year students. The analysis showed that the application of the developed decision support system allowed to increase class attendance by 24 % and academic performance, while the proportion of students receiving excellent grades increased by 9 %. The system application enables the experts (Universities administration and teachers as well as industrial program partners) to modernize the educational trajectory promptly. Lecturers can prepare in advance for a potential reorganization of the educational process in the event of changes in external conditions.

   The novelty of the proposed approach is to integrate into a single learning management system a variety of modern and traditional methods: interviewing, clustering, decision support, forecasting changes in external factors, building an educational trajectory considering the opinions of all participants in the educational process.

эффективная образовательная траекторияобучение магистровавтоматизированная системаметоды принятия решенийнеопределенность внешних факторовeffective educational trajectorymaster’s learningcomputer-aided systemdecision making methodsuncertainty of external factorsReferencesWong J. T., Hughes B. S. Leveraging learning experience design: Digital media approaches to influence motivational traits that support student learning behaviors in undergraduate online courses. Journal of Computing in Higher Education. 2023;35:595–632. DOI: 10.1007/s12528-022-09342-1.Wong J. T., Hughes B. S. Leveraging learning experience design: Digital media approaches to influence motivational traits that support student learning behaviors in undergraduate online courses. Journal of Computing in Higher Education. 2023;35:595–632. DOI: 10.1007/s12528-022-09342-1.Zou D., Xie H., Wang F. Effects of technology enhanced peer, teacher and self-feedback on students’ collaborative writing, critical thinking tendency and engagement in learning. Journal of Computing in Higher Education. 2023;35:166–185. DOI: 10.1007/s12528-022-09337-y.Zou D., Xie H., Wang F. Effects of technology enhanced peer, teacher and self-feedback on students’ collaborative writing, critical thinking tendency and engagement in learning. Journal of Computing in Higher Education. 2023;35:166–185. DOI: 10.1007/s12528-022-09337-y.Phan M., De Caigny A., Coussement K. A decision support framework to incorporate textual data for early student dropout prediction in higher education. Decision Support Systems. 2023;(168): Article 113940. DOI: 10.1016/j.dss.2023.113940.Phan M., De Caigny A., Coussement K. A decision support framework to incorporate textual data for early student dropout prediction in higher education. Decision Support Systems. 2023;(168): Article 113940. DOI: 10.1016/j.dss.2023.113940.Zha Sh., Jin Y., Wheeler R., Bosarge E. A mixed-method cluster analysis of physical computing and robotics integration in middle-grade math lesson plans. Computers & Education. 2022;190:Article 104623. DOI: 10.1016/j.compedu.2022.104623.Zha Sh., Jin Y., Wheeler R., Bosarge E. A mixed-method cluster analysis of physical computing and robotics integration in middle-grade math lesson plans. Computers & Education. 2022;190:Article 104623. DOI: 10.1016/j.compedu.2022.104623.Lung-Guang N. Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education. 2019;134:50–62. DOI: 10.1016/j.compedu.2019.02.004.Lung-Guang N. Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education. 2019;134:50–62. DOI: 10.1016/j.compedu.2019.02.004.Boelens R., Voet M., De Wever B. The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers & Education. 2018;120:197–212. DOI: 10.1016/j.compedu.2018.02.009.Boelens R., Voet M., De Wever B. The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers & Education. 2018;120:197–212. DOI: 10.1016/j.compedu.2018.02.009.Riestra-González M., Paule-Ruíz M. del P., Ortin F. Massive LMS log data analysis for the early prediction of course-agnostic student performance. Computers & Education. 2021;163:Article 104108. DOI: 10.1016/j.compedu.2020.104108.Riestra-González M., Paule-Ruíz M. del P., Ortin F. Massive LMS log data analysis for the early prediction of course-agnostic student performance. Computers & Education. 2021;163:Article 104108. DOI: 10.1016/j.compedu.2020.104108.Vainshtein Yu. V., Esin R. V., Tsibul’skii G. M. Adaptive model of developing individual educational trajectories for blended learning. Informatics and Education. 2017;(2):83–86. (In Russian.) EDN: YRUKSF.Vainshtein Yu. V., Esin R. V., Tsibul’skii G. M. Adaptive model of developing individual educational trajectories for blended learning. Informatics and Education. 2017;(2):83–86. (In Russian.) EDN: YRUKSF.Diachenko M. S., Leonov A. G. Solving the problem of automating the learning process through experimental search for an individual educational trajectory. Informatics and Education. 2024;39(4):14–26. (In Russian.) EDN: BMEVBI. DOI: 10.32517/0234-0453-2024-39-4-14-26.Diachenko M. S., Leonov A. G. Solving the problem of automating the learning process through experimental search for an individual educational trajectory. Informatics and Education. 2024;39(4):14–26. (In Russian.) EDN: BMEVBI. DOI: 10.32517/0234-0453-2024-39-4-14-26.Aldunin D. A. A model of recommendation service architecture for the formation of an individual educational trajectory for self-paced massive open online courses studying. Informatics and Education. 2024;39(1):74–83. (In Russian.) EDN: IBBOLE. DOI: 10.32517/0234-0453-2024-39-1-74-83.Aldunin D. A. A model of recommendation service architecture for the formation of an individual educational trajectory for self-paced massive open online courses studying. Informatics and Education. 2024;39(1):74–83. (In Russian.) EDN: IBBOLE. DOI: 10.32517/0234-0453-2024-39-1-74-83.Zakharchenkov K.V., Mrochek T.V., Kutuzov V.V. Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm. Informatics and Education. 2024;39(3):53–65. (In Russian.) EDN: XMLIRH. DOI: 10.32517/0234-0453-2024-39-3-53-65.Zakharchenkov K.V., Mrochek T.V., Kutuzov V.V. Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm. Informatics and Education. 2024;39(3):53–65. (In Russian.) EDN: XMLIRH. DOI: 10.32517/0234-0453-2024-39-3-53-65.Aljawarneh S. A. Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education. 2020;32:57–73. DOI: 10.1007/s12528-019-09207-0.Aljawarneh S. A. Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education. 2020;32:57–73. DOI: 10.1007/s12528-019-09207-0.Vishnekov A. V., Erokhina E. A., Ivanova E. M., Trubochkina N. K. Features of the educational process of training IT-specialists in the conditions of increasing capabilities of generative AI. Engineering Education. 2023;(34):123–135. (In Russian.) EDN: ZNKWCN. DOI: 10.54835/18102883_2023_34_11.Vishnekov A. V., Erokhina E. A., Ivanova E. M., Trubochkina N. K. Features of the educational process of training IT-specialists in the conditions of increasing capabilities of generative AI. Engineering Education. 2023;(34):123–135. (In Russian.) EDN: ZNKWCN. DOI: 10.54835/18102883_2023_34_11.Maatuk A. M., Elberkawi E. K., Aljawarneh S., Rashaideh H., Alharbi H. The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education. 2022;34:21–38. DOI: 10.1007/s12528-021-09274-2.Maatuk A. M., Elberkawi E. K., Aljawarneh S., Rashaideh H., Alharbi H. The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education. 2022;34:21–38. DOI: 10.1007/s12528-021-09274-2.Ivanova E. M., Vishnekov A. V. A computer design method of an effective educational trajectory in blended learning based on students’ assessment. Education and Information Technologies. 2020;25(2):1439–1458. DOI: 10.1007/s10639-020-10109-3.Ivanova E. M., Vishnekov A. V. A computer design method of an effective educational trajectory in blended learning based on students’ assessment. Education and Information Technologies. 2020;25(2):1439–1458. DOI: 10.1007/s10639-020-10109-3.Eddowes M., Stansfield R. Decision Making Techniques (ACCA). Sweet & Maxwell ltd; 1992. 688 p.Eddowes M., Stansfield R. Decision Making Techniques (ACCA). Sweet & Maxwell ltd; 1992. 688 p.Tabares M. S., Vallejo P., Montoya A., Correa D. A feedback model applied in a ubiquitous microlearning environment using SECA rules. Journal of Computing in Higher Education. 2022;34:462–488. DOI: 10.1007/s12528-021-09306-x.Tabares M. S., Vallejo P., Montoya A., Correa D. A feedback model applied in a ubiquitous microlearning environment using SECA rules. Journal of Computing in Higher Education. 2022;34:462–488. DOI: 10.1007/s12528-021-09306-x.Abuhassna H., Al-Rahmi W. M., Yahya N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. International Journal of Educational Technology in Higher Education. 2020;17:Article 38. DOI: 10.1186/s41239-020-00216-z.Abuhassna H., Al-Rahmi W. M., Yahya N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. International Journal of Educational Technology in Higher Education. 2020;17:Article 38. DOI: 10.1186/s41239-020-00216-z.Aljawarneh S., Lara J. A. Data science for analyzing and improving educational processes. Journal of Computing in Higher Education. 2021;33:545–550. DOI: 10.1007/s12528-021-09299-7.Aljawarneh S., Lara J. A. Data science for analyzing and improving educational processes. Journal of Computing in Higher Education. 2021;33:545–550. DOI: 10.1007/s12528-021-09299-7.Agarwal P. K., Mustafa N. H. k-means projective clustering. Proc. of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS ‘04). Association for Computing Machinery, New York, NY, USA; 2004:155–165. DOI: 10.1145/1055558.1055581.Agarwal P. K., Mustafa N. H. k-means projective clustering. Proc. of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS ‘04). Association for Computing Machinery, New York, NY, USA; 2004:155–165. DOI: 10.1145/1055558.1055581.Rousseeuw P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics. 1987;20:53–65. DOI: 10.1016/0377-0427(87)90125-7.Rousseeuw P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics. 1987;20:53–65. DOI: 10.1016/0377-0427(87)90125-7.Caliński T., Harabasz J. A dendrite method for cluster analysis. Communications in Statistics — Theory and Methods. 1974;3:1–27. DOI: 10.1080/03610927408827101.Caliński T., Harabasz J. A dendrite method for cluster analysis. Communications in Statistics — Theory and Methods. 1974;3:1–27. DOI: 10.1080/03610927408827101.Weng Y., Wang X., Hua J., Wang H., Kang M., Wang F.-Y. Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Transactions on Computational Social Systems. 2019;6(3):547–553. DOI: 10.1109/TCSS.2019.2914499.Weng Y., Wang X., Hua J., Wang H., Kang M., Wang F.-Y. Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Transactions on Computational Social Systems. 2019;6(3):547–553. DOI: 10.1109/TCSS.2019.2914499.Wu M. Neural network algorithm in regional macroeconomic forecast. 2022 IEEE 2<sup>nd</sup> Int. Conf. on Mobile Networks and Wireless Communications (ICMNWC). Tumkur, Karnataka, India; 2022:1–4. DOI: 10.1109/ICMNWC56175.2022.10031670.Wu M. Neural network algorithm in regional macroeconomic forecast. 2022 IEEE 2<sup>nd</sup> Int. Conf. on Mobile Networks and Wireless Communications (ICMNWC). Tumkur, Karnataka, India; 2022:1–4. DOI: 10.1109/ICMNWC56175.2022.10031670.Cuzzocrea A. Multidimensional big data analytics over big web knowledge bases: Models, issues, research trends, and a reference architecture. 2022 IEEE Eighth Int. Conf. on Multimedia Big Data (BigMM). Naples, Italy; 2022:1–6. DOI: 10.1109/BigMM55396.2022.00008.Cuzzocrea A. Multidimensional big data analytics over big web knowledge bases: Models, issues, research trends, and a reference architecture. 2022 IEEE Eighth Int. Conf. on Multimedia Big Data (BigMM). Naples, Italy; 2022:1–6. DOI: 10.1109/BigMM55396.2022.00008.Zhao Y., Shen Y., Qi Y. A security analysis of the Chinese robot supply chain based on open-source intelligence. 2021 IEEE 1<sup>st</sup> Int. Conf. on Digital Twins and Parallel Intelligence (DTPI). Beijing, China; 2021:219–222. DOI: 10.1109/DTPI52967.2021.9540101.Zhao Y., Shen Y., Qi Y. A security analysis of the Chinese robot supply chain based on open-source intelligence. 2021 IEEE 1<sup>st</sup> Int. Conf. on Digital Twins and Parallel Intelligence (DTPI). Beijing, China; 2021:219–222. DOI: 10.1109/DTPI52967.2021.9540101.Saaty T. L. Decision making for leaders: The analytic hierarchy process for decisions in a complex world. Pittsburgh, RWS Publications; 1980. 291 p.Saaty T. L. Decision making for leaders: The analytic hierarchy process for decisions in a complex world. Pittsburgh, RWS Publications; 1980. 291 p.Bell D. E. Regret in decision making under uncertainty. Operations Research. 1982;30(5):961–981. DOI: 10.1287/opre.30.5.961.Bell D. E. Regret in decision making under uncertainty. Operations Research. 1982;30(5):961–981. DOI: 10.1287/opre.30.5.961.Wald A. Statistical decision functions. N.Y., Wiley; L., Chapman & Hall; 1961. 179 p.Wald A. Statistical decision functions. N.Y., Wiley; L., Chapman & Hall; 1961. 179 p.Savage L. J. The theory of statistical decision. Journal of the American Statistical Association. 1951;46(1):55–67. DOI: 10.2307/2280094.Savage L. J. The theory of statistical decision. Journal of the American Statistical Association. 1951;46(1):55–67. DOI: 10.2307/2280094.Arrow K. J., Hurwicz L. Appendix: An optimality criterion for decision-making under ignorance. In: Arrow K. J., Hurwicz L. (eds) Studies in Resource Allocation Processes. Cambridge University Press; 1977:461–472. DOI: 10.1017/CBO9780511752940.015.Arrow K. J., Hurwicz L. Appendix: An optimality criterion for decision-making under ignorance. In: Arrow K. J., Hurwicz L. (eds) Studies in Resource Allocation Processes. Cambridge University Press; 1977:461–472. DOI: 10.1017/CBO9780511752940.015.Miyata Y. Laplace approximations and Bayesian information criteria in possibly misspecified models. Communications in Statistics — Theory and Methods. 2017;46(24):12240–12258. DOI: 10.1080/03610926.2017.1295079.Miyata Y. Laplace approximations and Bayesian information criteria in possibly misspecified models. Communications in Statistics — Theory and Methods. 2017;46(24):12240–12258. DOI: 10.1080/03610926.2017.1295079.Olivé D. M., Huynh D. Q., Reynolds M., Dougiamas M., Wiese D. A supervised learning framework: Using assessment to identify students at risk of dropping out of a MOOC. Journal of Computing in Higher Education. 2020;32(1):9–26. DOI: 10.1007/s12528-019-09230-1.Olivé D. M., Huynh D. Q., Reynolds M., Dougiamas M., Wiese D. A supervised learning framework: Using assessment to identify students at risk of dropping out of a MOOC. Journal of Computing in Higher Education. 2020;32(1):9–26. DOI: 10.1007/s12528-019-09230-1.Deeva G., Bogdanova D., Serral E., Snoeck M., De Weerdt J. A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education. 2021;162:Article 104094. DOI: 10.1016/j.compedu.2020.104094.Deeva G., Bogdanova D., Serral E., Snoeck M., De Weerdt J. A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education. 2021;162:Article 104094. DOI: 10.1016/j.compedu.2020.104094.Okogbaa J., Allen R. E., Sarpong D. F. Time spent at work and its impact on the academic performance of pharmacy students. International Journal of Environmental Research and Public Health. 2020;17(2):496. DOI: 10.3390/ijerph17020496.Okogbaa J., Allen R. E., Sarpong D. F. Time spent at work and its impact on the academic performance of pharmacy students. International Journal of Environmental Research and Public Health. 2020;17(2):496. DOI: 10.3390/ijerph17020496.Buckner E., Zhang Y., Gerardo L. B. The impact of COVID-19 on international student enrolments in North America: Comparing Canada and the United States. Higher Education Quarterly. 2021;76(2):328–342. DOI: 10.1111/hequ.12365.Buckner E., Zhang Y., Gerardo L. B. The impact of COVID-19 on international student enrolments in North America: Comparing Canada and the United States. Higher Education Quarterly. 2021;76(2):328–342. DOI: 10.1111/hequ.12365.

The authors declare that there are no conflicts of interest present.