Science for Education Today, 2020, vol. 10, no. 6, pp. 143–161
UDC: 
004.89

The impact of e-learning and social parameters on students’ academic performance [In English]

Petrusevich D. 1 (Moscow, Russian Federation)
1 Russian Technological University (MIREA)
Abstract: 

Introduction. The article examines the problem of assessing students' academic performance in the current situation.
The purpose of the paper is to evaluate the influence of e-learning and some social and behavioral parameters on students’ academic performance.
Materials and Methods. The author employed the machine learning procedures in order to identify and assess the current problems of the educational system, students’ behavior, and universities’ policy. Methods of mathematical analysis and statistics as well as ensemble methods (gradient boosting and the random forest algorithms) were used in order to achieve high accuracy of the research.
Results. The author conducted the analysis of the following datasets devoted to academic performance at higher and secondary educational institutions in a number of countries: Students’ Performance in Portugal, E-learning Student Reactions and Students’ Academic Performance.
The purposes of the current study were to identify statistical correlations between social parameters of students and the level of their academic performance and to understand how academic performance is determined by the implementation of online learning and blended learning.
The research findings suggest that mathematical statistics and data analysis methods allow to identify correlations between students’ performance data and reveal hidden relationships which can be important for university staff.
Conclusions. In conclusion, the author summarizes the results of evaluating the impact of the introduction of e-learning elements and some social parameters on students’ academic performance.

Keywords: 

Clustering students; Blended learning; Academic performance evaluation; Digitalization of education; Digital technologies in education; Correlation of features; performance improvement.

URL WoS/RSCI: https://www.webofscience.com/wos/rsci/full-record/RSCI:44478886

Prominence Percentile SciVal: 98.481 Student Performance | School Dropout | Moodle

https://www.scopus.com/record/display.uri?eid=2-s2.0-85099458333&origin=...

The impact of e-learning and social parameters on students' academic performance

For citation:
Petrusevich D. The impact of e-learning and social parameters on students’ academic performance [In English]. Science for Education Today, 2020, vol. 10, no. 6, pp. 143–161. DOI: http://dx.doi.org/10.15293/2658-6762.2006.08
References: 
  1. Amrieh E. A., Hamtini T., Aljarah I. Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 2016, vol. 9 (8), pp. 119–136. DOI: https://doi.org/10.14257/ijdta.2016.9.8.13
  2. Andrianova E. G., Golovin S. A., Zykov S. V., Lesko S. A., Chukalina E. R. Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems. Russian Technological Journal, 2020, vol. 8 (1), pp. 7–45. (In Russian) DOI: https://doi.org/10.32362/2500-316X-2020-8-4-7-45 URL: https://elibrary.ru/item.asp?id=43756167
  3. Li Y., Allen J., Casillas A. Relating psychological and social factors to academic performance: A longitudinal investigation of high-poverty middle school students. Journal of Adolescence, 2017, vol. 56, pp. 179–189. DOI: https://doi.org/10.1016/j.adolescence.2017.02.007
  4. Gimenez G., Martín-Oro Á., Sanaú J. The effect of districts’ social development on student performance. Studies in Educational Evaluation, 2018, vol. 58, pp. 80–96. DOI: https://doi.org/10.1016/j.stueduc.2018.05.009
  5. Law K. M. Y., Geng S., Li T. Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 2019, vol. 136, pp. 1–12. DOI: https://doi.org/10.1016/j.compedu.2019.02.021
  6. Salameh W., Sathakathulla A. The Impact of Social-Economic Factors on Students’ English Language Performance in EFL Classrooms in Dubai. English Language and Literature Studies, 2018, vol. 8 (4), pp. 110. DOI: https://doi.org/10.5539/ells.v8n4p110
  7. Mushtaq B., Jyotsna J. Effect of Socio Economic Status on Academic Performance of Secondary School Students. The International Journal of Indian Psychology, 2016, vol. 3 (4), pp. 56. DOI: https://doi.org/10.13140/RG.2.2.19730.71369
  8. Ishizaka A., Lokman B., Tasiou M. A Stochastic Multi-criteria Divisive Hierarchical Clustering Algorithm. Omega, 2020, pp. 102370.  DOI: https://doi.org/10.1016/j.omega.2020.102370
  9. Anfyorov M. A. Genetic clustering algorithm. Russian Technological Journal, 2019, vol. 7 (6), pp.  134–150 (In Russian) DOI: https://doi.org/10.32362/2500-316X-2019-7-6-134-150  URL: https://www.elibrary.ru/item.asp?id=42347089
  10. Asrial M., Habibi A., Mukminin A., Hadisaputra P. Science teachers’ integration of digital resources in education: A survey in rural areas of one Indonesian province. Heliyon, 2020, vol.  6  (8), pp. e04631. DOI: https://doi.org/10.1016/j.heliyon.2020.e04631
  11. Sarkisov S. S., Lomonosova N. V., Zolkina A. V., Sarkisov T. S. Integration of digital technologies in mining and metallurgy industries. Tsvetnye Metally, 2020, vol. 2020, pp. 7–14. DOI: http://dx.doi.org/10.17580/tsm.2020.03.01
  12. Lomonosova N. V., Zolkina A. V. Digital learning resources: Enhancing efficiency within blended higher education. Novosibirsk State Pedagogical University Bulletin, 2018, vol. 8 (6), pp. 121–137. DOI: http://dx.doi.org/10.15293/2226-3365.1806.08 URL: https://www.elibrary.ru/item.asp?id=36655296
  13. Demenkova T. A., Tomashevskaya V. S., Shirinkin I. S. Mobile applications for tasks of distance learning. Russian Technological Journal, 2018, vol. 6 (1), pp. 5–19. (In Russian) URL: https://elibrary.ru/item.asp?id=32466033 
  14. Alkhowailed M. S., Rasheed Z., Shariq A., Elzainy A., Sadik A.E., Alkhamiss A., Alsolai A. M., Alduraibi S. K., Alduraibi A., Alamro A., Alhomaidan H. T., Al Abdulmonem W. Digitalization plan in medical education during COVID-19 lockdown. Informatics in Medicine Unlocked, 2020, vol. 20, pp. 100432. DOI: https://doi.org/10.1016/j.imu.2020.100432
  15. Andrade H. L. A Critical Review of Research on Student Self-Assessment. Frontiers in Education, 2019, vol. 4, pp. 87. DOI: https://doi.org/10.3389/feduc.2019.00087
  16. Aricò F. R., Lancaster S. J. Facilitating active learning and enhancing student self-assessment skills. International Review of Economics Education, 2018, vol. 29, pp. 6–13. DOI: https://doi.org/10.1016/j.iree.2018.06.002
  17. Piper K., Morphet J., Bonnamy J. Improving student-centered feedback through self-assessment. Nurse Education Today, 2019, vol. 83, pp. 104193. DOI: https://doi.org/10.1016/j.nedt.2019.08.011
  18. Panadero E., Brown G. L., Strijbos J.-W. The future of student self-assessment: a review of known unknowns and potential directions. Educational Psychology Review, 2016, vol. 28 (4), pp. 803–830. DOI: https://doi.org/10.1007/s10648-015-9350-2
  19. Sharma R., Amit J., Gupta N. Garg S., Batta M., Dhir S. Impact of self-assessment by students on their learning. International Journal of Applied and Basic Medical Research, 2016, vol. 6 (3), pp.  226. DOI: https://doi.org/10.4103/2229-516X.186961
  20. Erkens M., Bodemer D. Improving collaborative learning: Guiding knowledge exchange through the provision of information about learning partners and learning contents. Computers & Education, 2019, vol. 128, pp. 452–472. DOI: https://doi.org/10.1016/j.compedu.2018.10.009
  21. Liao C.-W., Chen C.-H. & Shih S.-J. The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 2019, vol. 133, pp. 43–55. DOI: https://doi.org/10.1016/j.compedu.2019.01.013 
  22. Hernández-Sellés N., Muñoz-Carril P.-C., González-Sanmamed M. Computer-supported collaborative learning: an analysis of the relationship between interaction, emotional support and online collaborative tools. Computers & Education, 2019, vol. 138, pp. 1–12. DOI: https://doi.org/10.1016/j.compedu.2019.04.012
  23. Díaz-Ramírez J. Gamification in engineering education – An empirical assessment on learning and game performance. Heliyon, 2020, vol. 6 (9), pp. e04972. DOI: https://doi.org/10.1016/j.heliyon.2020.e04972
  24. Zolkina A. V., Lomonosova N. V., Petrusevich D. A. Gamification as a tool of enhancing teaching and learning effectiveness in higher education: Needs analysis. Science for Education Today, 2020, vol. 10 (3), pp. 127–143. (In Russian) DOI: http://dx.doi.org/10.15293/2658-6762.2003.07
  25. Landers R. N., Landers A. K. An empirical test of the theory of gamified learning. The effect of leaderboards on time-on-task and academic performance. Simulation & Gaming, 2015, vol. 45 (6), pp. 769–785. DOI: http://dx.doi.org/10.1177/1046878114563662
  26. Rastrollo-Guerrero J. L., Gómez-PulidoJ. A., Durán-Domínguez A. Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review. Applied Sciences, 2020, vol.  10  (3), pp. 1042. DOI: https://doi.org/10.3390/app10031042
  27. Asif R., Merceron A., Ali S. A., Haider N. G. Analyzing undergraduate students' performance using educational data mining. Computers & Education, 2017, vol. 113, pp. 177–194. DOI: https://doi.org/10.1016/j.compedu.2017.05.007
  28. Fernandes E., Holanda M., Victorino M., Borges V., Carvalho R., Erven G. V. Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 2019, vol. 94, pp. 335–343. DOI: https://doi.org/10.1016/j.jbusres.2018.02.012
  29. Yang F., Li F. W. B. Study on student performance estimation, student progress analysis, and student potential prediction based on data mining. Computers & Education, 2018, vol. 123, pp. 97–108. DOI: https://doi.org/10.1016/j.compedu.2018.04.006
  30. Ripoll V., Godino-Ojer M., Calzada J. Teaching Chemical Engineering to Biotechnology students in the time of COVID-19: assessment of the adaptation to digitalization. Education for Chemical Engineers, 2020, vol. 34, pp. 94–105. DOI: https://doi.org/10.1016/j.ece.2020.11.005
  31. Mishra L., Gupta T., Shree A. Online Teaching-Learning in Higher Education during Lockdown Period of COVID-19 Pandemic. International Journal of Educational Research Open, 2020, pp.  1000012. DOI: https://doi.org/10.1016/j.ijedro.2020.100012
Date of the publication 31.12.2020