Managing the University’s Educational Process Based on Predicting Students’ Academic Performance
The authors of the article present a comprehensive analysis of the accounting of students’ academic performance in the management of the educational process of the university. The information about students that affects their academic performance and satisfaction with the educational organization is...
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| Format: | Article |
| Language: | English |
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Moscow Polytechnic University
2024-12-01
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| Series: | Высшее образование в России |
| Subjects: | |
| Online Access: | https://vovr.elpub.ru/jour/article/view/5241 |
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| author | E. V. Alikina D. V. Maltsev |
| author_facet | E. V. Alikina D. V. Maltsev |
| author_sort | E. V. Alikina |
| collection | DOAJ |
| description | The authors of the article present a comprehensive analysis of the accounting of students’ academic performance in the management of the educational process of the university. The information about students that affects their academic performance and satisfaction with the educational organization is analyzed and classified. The focus of the study is on the application of predictive models in the management of the educational process in order to adapt the content of disciplines to the current contingent of students. The study used data only on first-year students (2023/24 academic year) of bachelor’s and specialist’s degree levels (n=1549). The information is depersonalized and contains the following data: demographic (age, gender, citizenship), social (socio-cultural environment, place of residence, place of residence during study), academic (previous education, results of entrance tests, current academic performance, faculty, qualification level), economic (scholarship, type of competition – budget/contract). Methods of mathematical statistics were used to analyze the data: determining the type of data distribution using the Shapiro-Wilk test, establishing the presence of multicollinearity in the construction of multiple regression by the Pearson criterion, establishing correlation dependencies by Spearman’s rank correlation method. Machine learning methods are implemented in the Python programming language (v. 3.8) using the freely distributed Keras library. The main results. The classification of factors affecting the academic performance and satisfaction of students is presented. Using the methods of mathematical statistics, the importance of each factor for predicting academic performance has been established. An educational process management model based on Agile Learning Design has been developed and presented, which allows adapting a specific discipline to the current contingent of students. |
| format | Article |
| id | doaj-art-a30f5259766a49b98c036fffc7c79a84 |
| institution | Kabale University |
| issn | 0869-3617 2072-0459 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Moscow Polytechnic University |
| record_format | Article |
| series | Высшее образование в России |
| spelling | doaj-art-a30f5259766a49b98c036fffc7c79a842025-02-01T13:14:33ZengMoscow Polytechnic UniversityВысшее образование в России0869-36172072-04592024-12-01331113214810.31992/0869-3617-2024-33-11-132-1482516Managing the University’s Educational Process Based on Predicting Students’ Academic PerformanceE. V. Alikina0D. V. Maltsev1Perm National Research Polytechnic UniversityPerm National Research Polytechnic UniversityThe authors of the article present a comprehensive analysis of the accounting of students’ academic performance in the management of the educational process of the university. The information about students that affects their academic performance and satisfaction with the educational organization is analyzed and classified. The focus of the study is on the application of predictive models in the management of the educational process in order to adapt the content of disciplines to the current contingent of students. The study used data only on first-year students (2023/24 academic year) of bachelor’s and specialist’s degree levels (n=1549). The information is depersonalized and contains the following data: demographic (age, gender, citizenship), social (socio-cultural environment, place of residence, place of residence during study), academic (previous education, results of entrance tests, current academic performance, faculty, qualification level), economic (scholarship, type of competition – budget/contract). Methods of mathematical statistics were used to analyze the data: determining the type of data distribution using the Shapiro-Wilk test, establishing the presence of multicollinearity in the construction of multiple regression by the Pearson criterion, establishing correlation dependencies by Spearman’s rank correlation method. Machine learning methods are implemented in the Python programming language (v. 3.8) using the freely distributed Keras library. The main results. The classification of factors affecting the academic performance and satisfaction of students is presented. Using the methods of mathematical statistics, the importance of each factor for predicting academic performance has been established. An educational process management model based on Agile Learning Design has been developed and presented, which allows adapting a specific discipline to the current contingent of students.https://vovr.elpub.ru/jour/article/view/5241student academic performanceacademic performance forecastneural networkcontingent retentionadaptability of educationartificial intelligenceagile learning design |
| spellingShingle | E. V. Alikina D. V. Maltsev Managing the University’s Educational Process Based on Predicting Students’ Academic Performance Высшее образование в России student academic performance academic performance forecast neural network contingent retention adaptability of education artificial intelligence agile learning design |
| title | Managing the University’s Educational Process Based on Predicting Students’ Academic Performance |
| title_full | Managing the University’s Educational Process Based on Predicting Students’ Academic Performance |
| title_fullStr | Managing the University’s Educational Process Based on Predicting Students’ Academic Performance |
| title_full_unstemmed | Managing the University’s Educational Process Based on Predicting Students’ Academic Performance |
| title_short | Managing the University’s Educational Process Based on Predicting Students’ Academic Performance |
| title_sort | managing the university s educational process based on predicting students academic performance |
| topic | student academic performance academic performance forecast neural network contingent retention adaptability of education artificial intelligence agile learning design |
| url | https://vovr.elpub.ru/jour/article/view/5241 |
| work_keys_str_mv | AT evalikina managingtheuniversityseducationalprocessbasedonpredictingstudentsacademicperformance AT dvmaltsev managingtheuniversityseducationalprocessbasedonpredictingstudentsacademicperformance |