Analysis and Forecasting Students’ Academic Performance Using a Digital Educational Environment
https://doi.org/10.31992/0869-3617-2021-30-8-9-125-133
Abstract
Analysis and Forecasting Students’ Academic Performance Using a Digital Educational Environment The article discusses technical solutions used at Orenburg State University to organize a digital educational environment. Also, the authors have studied the academic performance of technical and humanities students during the period of face-to-face education and during the lockdown period. The analysis of academic performance shows the absence of significant deviations in one direction or another. The key internal and external factors that influence the students’ academic performance are highlighted. It can be concluded that the use of both internal and external factors gives a high accuracy in predicting the final progress of students.
Keywords
Supporting agencies: The research work was funded by the Russian Federation Presidential grant for the state support of leading scientific schools (NSh-2502.2020.9).
About the Authors
A. E. Shukhman
Orenburg State University
Russian Federation
Russian Federation
Alexander E. Shukhman – Cand. Sci. (Education) Head of the Department of Geometry and Computer Science
13, Prospekt Pobedy, Orenburg, 460018
D. I. Parfenov
Orenburg State University
Russian Federation
Russian Federation
Denis I. Parfenov – Cand. Sci. (Engineering), Head of the Department of digital educational platforms
13, Prospekt Pobedy, Orenburg, 460018
L. L. Legashev
Orenburg State University
Russian Federation
Russian Federation
Leonid L. Legashev – Cand. Sci. (Engineering), Head of the Department for Distant Learning Technologies
13, Prospekt Pobedy, Orenburg, 460018
L. S. Grishina
Orenburg State University
Russian Federation
Russian Federation
Lyubov S. Grishina – Lecturer at the Applied Mathematics Department
13, Prospekt Pobedy, Orenburg, 460018
References
1. Romero, C., Ventura, S. (2020). Educational Data Mining and Learning Analytics: An Updated Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. Vol. 10, no. 3, pp. 1-21, doi: https://doi.org/10.1002/widm.1355
2. Salloum, S.A. Alshurideh, M., Elnagar, A, Shaalan, K. (2020). Mining in Educational Data: Review and Future Directions. Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 92-102, doi: https://doi.org/10.1007/978-3-030-44289-7_9
3. Chen, G., Rolim, V., Ferreira Mello, R., Gašević, D. (2020). Let’s Shine Together! A Comparative Study between Learning Analytics and Educational Data Mining. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 544-553, doi: https://doi.org/10.1145/3375462.3375500
4. Injadat, M.N., Moubayed, A., Nassif, A.B., Shami, A. (2020). Systematic Ensemble Model Selection Approach for Educational Data Mining. Knowledge-Based Systems. Vol. 200, pp. 1-16, doi: https://doi.org/10.1016/j.knosys.2020.105992
5. Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., Ragos, O. (2020). Implementing AutoML in Educational Data Mining for Prediction Tasks. Applied Sciences. Vol. 10, no. 1, pp. 90, doi: https://doi.org/10.3390/app10010090
6. Sutoyo, E., Almaarif, A. (2020). Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi). Vol. 4, no. 1, pp. 95-101, doi: https://doi.org/10.29207/resti.v4i1.1502
7. bin Mat, U. (2013). An Overview of Using Academic Analytics to Predict and Improve Students’ Achievement: A Proposed Proactive Intelligent Intervention. IEEE 5th Conference on Engineering Education (ICEED). Pp. 126-130, doi: 10.1109/ICEED.2013.6908316
8. Simsek, A., Balaban, J. (2010). Learning Strategies of Successful and Unsuccessful University Students. Contemporary Educational Technology. Vol. 1, no. 1, pp. 36-45, doi: https://doi.org/10.30935/cedtech/5960
9. Oladokun, V., Adebanjo, A., Charles-Owaba, O. (2008). Predicting Students Academic Performance Using Artificial Neural Network: A Case Study of an Engineering Course. The Pacific Journal of Science and Technology. Vol. 9, no. 1, pp. 72-79. Available at: http://www.akamaiuniversity.us/PJST9_1_72.pdf (accessed 24.07.2021).
10. Salal, Y.K., Abdullaev, S.M. (2019). Using of Data Mining Techniques to Predict of Student’s Performance in Industrial Institute of Al-Diwaniyah, Iraq. Vestnik Yuzhno-Ural’skogo gosudarstvennogo universiteta. Seriya: Kompyuternye tekhnologii, upravlenie, radioelektronika = Bulletin of the South Ural State University. Series «Computer Technologies, Automatic Control & Radioelectronics». Vol. 19, no. 1, pp. 121-130, doi: 10.14529/ctcr190111
Review
Views:
720
Email this article 