Application of Statistical Models for the Analysis of Data Obtained from Continuous Assessment of Students in Higher Education

Sanja Stojanović; Radovan Dragić; Gabrijela Dimić; Čedomir Vasić; Zoran Gordić

doi:10.23947/2334-8496-2025-13-3-749-764

Authors

Sanja Stojanović Educons University, PM College, Belgrade, Serbia https://orcid.org/0000-0002-1566-2758
Radovan Dragić University Business Academy in Novi Sad, Faculty of Economics and Engineering Management, Serbia https://orcid.org/0009-0005-0691-4481
Gabrijela Dimić Academy of Technical and Art Applied Studies, Belgrade, Serbia https://orcid.org/0000-0003-4233-540X
Čedomir Vasić MB University Belgrade, Faculty of Business and Law, Belgrade, Serbia https://orcid.org/0009-0008-6634-6465
Zoran Gordić AsyTech Limited Honk Kong, Honk Kong, China https://orcid.org/0009-0004-0024-0335

DOI:

https://doi.org/10.23947/2334-8496-2025-13-3-749-764

Keywords:

Statistical Models, Statistical Inference, Database of Statistical Data, Continuous Assessment (ECTS), Higher Education

Abstract

This paper presents the findings of a study aimed at exploring students activities during lectures and exercises. The research was conducted through basic population of students at the University “St. Kliment Ohridski” – Bitola, in academic years from 2015 to 2023. The data obtained from the continuous checking of the students’ knowledge, which refer to: attendance and activity in lectures and exercises, preparation of seminar papers, independent (home) work, completed or realized projects / programs, as well as from the colloquium grades, are the basis for the application of linear statistical models that will be realized through descriptive, correlation and regression analysis, factor analysis and statistical inference. In this way, information is obtained from a series of indicators that will serve the professors to take appropriate corrective actions, in order to improve and better create their teaching and educational process. As a result, it is expected to obtain better results that are of interest to students and higher education institutions, in terms of increasing the quality and efficiency of the teaching and learning process in higher education.

Downloads

Download data is not yet available.

References

Abbas, J. (2020). HEISQUAL: A modern approach to measure service quality in higher education institutions. Studies in Educational Evaluation, 67, 100933. https://doi.org/10.1016/j.stueduc.2020.100933 DOI: https://doi.org/10.1016/j.stueduc.2020.100933

Bencsik, H. A. (2017). The Most Preffered Methods in Higher Education. In T. E. EpSBS (Ed.), 8th ICEEPSY 2017 The International Conference on Education and Educational Psychology.

Bowman, N. A. (2012). Effect sizes and statistical methods for meta-analysis in higher education. Research in Higher Education, 53(3), 375-382. https://doi.org/10.1007/s11162-011-9232-5 DOI: https://doi.org/10.1007/s11162-011-9232-5

Castillo-Manzano, J. I., Castro-Nuno, M., López-Valpuesta, L., Sanz-Diaz, M. T., & Yñiguez, R. (2024). Evaluating the design of digital tools for the transition to an e-continuous assessment in higher education. Journal of Computing in Higher Education, 36(3), 875-893. https://doi.org/10.1007/s12528-023-09381-2 DOI: https://doi.org/10.1007/s12528-023-09381-2

Clemons, R., & Jance, M. (2024). Defining quality in higher education and identifying opportunities for improvement. Sage Open, 14(3), https://doi.org/10.1177/21582440241271155 DOI: https://doi.org/10.1177/21582440241271155

Combrinck, M., & Hatch, M. (2012). Students’ Experiences of a Continuous Assessment Approach at a Higher Education Institution. Journal of Socil Sciences, 33(1), 81-89. https://library.iated.org/view/COMBRINCK2012STU DOI: https://doi.org/10.1080/09718923.2012.11893088

Kutner, M., Nachtsheim, C., Neter, J., & Li, W. (2004). Applied Linear Statistical Models with Student Data CD, 5th Edition. ISBN: 0073108742, pr (Vol. 5th edition).

Luan, H., & Tsai, C. C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250-266. https://www.jstor.org/stable/26977871

Morales, M., Salmerón, A., Maldonado, A. D., Masegosa, A. R. & Rumí, R. (2022). An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark. Mathematics, 10(21), 3994. https://doi.org/10.3390/math10213994 DOI: https://doi.org/10.3390/math10213994

Playfoot, D., Wilkinson, L. L., & Mead, J. (2023). Is continuous assessment inclusive? An analysis of factorsinfluencing student grades? Assessment & Evaluation in Higher Education, 48(7), 938-950. https://doi.org/10.1080/02602938.2022.2150834 DOI: https://doi.org/10.1080/02602938.2022.2150834

Rincón, Y. R., Munárriz, A., & Ruiz, A. M. (2024). A new approach to continuous assessment: Moving from a stressful sum of grades to meaningful learning through self-reflection. The International Journal of Management Education, 22(3), 101-128. https://doi.org/10.1016/j.ijme.2024.101072 DOI: https://doi.org/10.1016/j.ijme.2024.101072

Romanas, J., & Dalius, P. (2011). On the mistakes of applications of statistical methods in the Educational investigations. Mathematical Education and Logical Thinking project.

Smeds, M. (2022). Deming’s tampering revisited: Definition and future research agenda. International Journal of Quality and Service Sciences, 14(5), 37–51. https://doi.org/10.1108/IJQSS-03-2021-0041. DOI: https://doi.org/10.1108/IJQSS-03-2021-0041

Spitzig, J., & Renner, B. J. (2022). Student Engagement and Retention of Adult Learners at Community Colleges. Journal of College Student Retention: Research, Theory & Practice, 26(4), 1010-1027. https://doi.org/10.1177/15210251221138065 DOI: https://doi.org/10.1177/15210251221138065

Teeroovengadum V., Nunkoo R., Gronroos C. & Kamalanabhan T. J. & Seebaluck A. K. (2019). Higher education service quality, student satisfaction and loyalty. Quality Assurance in Education, 27(4), 427–445. https://doi.org/10.1108/QAE-01-2019-0003 DOI: https://doi.org/10.1108/QAE-01-2019-0003

Trung, D., Dudić, B., Duc, D. V., Son, H. N., & Mittelman, A. (2024). Building a Ranking System for Lecturers Based on Student Evaluations in Teaching a Specific Course: A Case Study at a University in Vietnam. International Journal of Cognitive Research in Science, Engineering and Education, 12(2), 335-350. https://doi.org/10.23947/2334-8496-2024-12-2-335-350 DOI: https://doi.org/10.23947/2334-8496-2024-12-2-335-350

Tüzüntürk, S. (2015). Performing Parametric and Nonparametric Statistical Methods Using Higher Education Service Quality Data. PARADOKS Ekonomi, Sosyoloji ve Politika Dergisi PARADOKS Economics, Sociology and Policy Journal, 11(1), 265-293. https://dergipark.org.tr/en/pub/paradoks/issue/16347/171195

Williamson, B., Bayne, S. & Shay, S. (2020). The dataﬁcation ofteaching in Higher Education: critical issues and perspec-tives. Teaching in Higher Education, 25(4), 351–365. https://doi.org/10.1080/13562517.2020.1748811 DOI: https://doi.org/10.1080/13562517.2020.1748811

Wong, B.T.M. & Li, K.C. (2020). A review of learning analyticsintervention in higher education (2011-2018). Journal of Computers in Education, 7(1), 7–28. https://doi.org/10.1007/s40692-019-00143-7 DOI: https://doi.org/10.1007/s40692-019-00143-7

Yang, X. & Ge, J. (2022). Predicting Student Learning Effectiveness in Higher Education Based on Big Data Analysis. Mobile Information Systems, 8409780. https://doi.org/10.1155/2022/8409780 DOI: https://doi.org/10.1155/2022/8409780