Developing group and individual performance paths based on e-learning platform data
- Authors: Vladova A.Y.1
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Affiliations:
- V.A. Trapeznikov Institute of Control Sciences of RAS, Moscow, Financial University under the Government of Russian Federation
- Issue: No 111 (2024)
- Pages: 179-196
- Section: Control of social-economic systems
- URL: https://bakhtiniada.ru/1819-2440/article/view/289120
- DOI: https://doi.org/10.25728/ubs.2024.111.7
- ID: 289120
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Abstract
Maintaining a high level of education is a key task in university management. Despite continuous monitoring of student performance, educational institution management fails to adequately utilize performance forecasting methods when shaping student learning paths. The proposed approach differs from existing ones in several aspects. Firstly, it analyzes features containing grades for various assignments completed by students on the e-learning platform, expanding the feature space by normalizing grades on a single scale and creating new features: an index and changes in performance for different types of assignments. Secondly, it identifies students at academic risk. Thirdly, it predicts exam scores for each student using a linear regression model. Fourthly, it groups students with similar learning trajectories for personalized consultations. The approach to predicting exam results for individual students demonstrates a commitment to providing comprehensive support beyond simple assessment. Through analysis, modeling, and personalized consultations, the research aims to proactively enhance academic performance in university settings.
About the authors
Alla Yur'evna Vladova
V.A. Trapeznikov Institute of Control Sciences of RAS, Moscow, Financial University under the Government of Russian Federation
Email: avladova@ipu.ru
Moscow
References
- АРЗАМАСЦЕВ С. А., БГАТОВ М., СЕМЕНЧИКОВ Д. Предсказание оттока абонентов: сравнение методов машинного обучения // Computer Tools in Education. – 2018. – №5. – C. 5–23.
- ВЛАДОВА А.Ю. Кластерный анализ изменений про-странственного положения трубных секций маги-стрального нефтепровода по данным внутритрубных обследований // Безопасность труда в промышленности. – 2018. – №1. – C. 22–25.
- ВЛАДОВА А.Ю. Формирование пространства призна-ков и авторегрессионных моделей для прогноза отступ-лений железнодорожного полотна // Проблемы управ-ления. – 2023. – Вып. 2. – C. 54–64.
- ВЛАДОВА А.Ю., ШЕК Е.Д. Подготовка данных для ма-шинного анализа ключевых показателей эффективности территориальных менеджеров // Бизнес-информатика. – 2021. – №3. – C. 48–59.
- ЗАДАДАЕВ С.А., ОРЛОВА И.В. Опыт применения эко-нометрического инструментария для прогнозирования показателей национальных целей развития РФ // Фун-даментальные исследования. – 2022. – №10-1. – С. 54–59. – doi: 10.17513/fr.43343.
- КУРАВСКИЙ Л.С., ЮРЬЕВ Г.А., ЗЛАТОМРЕЖЕВ В.И. и др. Оценка действий экипажа воздушного судна на ос-нове модели рисков человеческого фактора // Экспери-ментальная психология. – 2020. – Т. 13, №2. – С. 153–181. – doi: 10.17759/exppsy.2020130211.
- BAKHTADZE N., ZAIKIN O., ZYLAWSKI A. и др. Incen-tive Model of a Project Learning Process // 7th Int. Conf. on Frontiers of Industrial Engineering, ICFIE-2020: Virtual, Sin-gapore, 2020. – P. 73–81. – doi: 10.1109/ICFIE50845.2020.9266738.
- BAYAZIT A., ILGAZ H., ERDEN S. Profiling students via clustering in a flipped clinical skills course using learning analytics // Medical Teacher. – 2022. – №7(45). – C. 724–731.
- KIM J., CHO J. DBSCAN-Based Tracklet Association An-nealer for Advanced Multi-Object Tracking // Sensors. – 2021. – Vol. 21. – P. 5715.
- LIU M., YU D. Towards intelligent E-learning systems // Ed-ucation and Information Technologies. – 2023. – No. 7(28). – P. 7845–7876.
- NAGY V., DUMA L. Measuring efficiency and effectiveness of knowledge transfer in e-learning // Heliyon. – 2023. – No. 7(9).
- OLUWADELE D., SINGH Y., ADELIYI T. E-Learning Per-formance Evaluation in Medical Education – A Bibliometric and Visualization Analysis // Healthcare. – 2023. – No. 11. – P. 232.
- TROUSSAS C., VIRVOU M., ALEPIS E. Comulang: to-wards a collaborative e-learning system that supports stu-dent group modeling // SpringerPlus. – 2013. – No. 1(2). – P. 387.
- YE M., SHENG X. SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction // Sensors. – 2022. – No. 22(22). – P. 8838.
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