Share:


Multi-criteria ranking of organizational factors affecting the learning quality outcomes in elementary education in Serbia

Abstract

The research within this paper is motivated by the opinion that different organizational factors in primary education can have a stronger or weaker impact on the quality of the learning outcome. Organizational factors, criteria analyzed in this paper, are school management, school infrastructure, students’ foreknowledge, teacher competencies, curriculum content, student motivation, and the quality of the teaching process. Using SWARA (Step-wise Weight Assessment Ratio Analysis) method of multi-criteria decision-making, the answers of elementary school principals, members of the panel of experts, were processed. The calculation within this method was performed using fuzzy numbers to ensure the reliability of expert evaluations. The results of the applied method, in the form of weighting coefficients of the criteria, indicate that school management has an influence on the selection and building of teachers’ competencies while the given competence can indirectly affect the overall success of students through the establishment of an adequate school infrastructure, which affects the knowledge quality. Knowing the factor that has the highest impact enables principals to manage this factor and contribute to enhancing the knowledge quality. This research contributes to raising awareness of the importance of particular organizational factors in elementary education and the need to improve them.


First published online 28 October 2020

Keyword : SWARA method, fuzzy number, organizational factors, quality of learning outcomes, elementary education, school management, teacher competencies

How to Cite
Epifanić, V., Urošević, S., Dobrosavljević, A., Kokeza, G., & Radivojević, N. (2021). Multi-criteria ranking of organizational factors affecting the learning quality outcomes in elementary education in Serbia. Journal of Business Economics and Management, 22(1), 1-20. https://doi.org/10.3846/jbem.2020.13675
Published in Issue
Jan 27, 2021
Abstract Views
1262
PDF Downloads
895
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abdi, K., Mardani, A., Senin, A., Tupenaite, L., Naimaviciene, J., Kanapeckiene, L., & Kutut, V. (2018). The effect of knowledge management, organizational culture and organizational learning on innovation in automotive industry. Journal of Business Economics and Management, 19(1), 1–19. https://doi.org/10.3846/jbem.2018.1477

Aghdaie, M. H., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2014). Synergies of data mining and multiple attribute decision making. Procedia – Social and Behavioral Sciences, 110, 767–776. https://doi.org/10.1016/j.sbspro.2013.12.921

Alibabić, Š. (2009). Profesionalizacijamenadžmenta u obrazovanju. Obrazovanje odraslih, IX(2), 9–20.

Allan, J. (1996). Learning outcomes in Higher Education. Studies in Higher Education, 21(1), 93–108. https://doi.org/10.1080/03075079612331381487

Araujo, M. C., Carneiro, P., Cruz-Aguayo, Y., & Schady, N. (2016). Teacher quality and learning outcomes in kindergarten. The Quarterly Journal of Economics, 131(3), 1415–1453. https://doi.org/10.1093/qje/qjw016

Arsić, M., Urošević, S., Nikolić, Đ., & Voza, D. (2011, May 26-28). Ispitivanje zadovoljstva zaposlenih u obrazovnim institucijama. In VII Majska konferencija o strategijskom menadžmentu (pp. 390–400). Technical Faculty in Bor, Zaječar, Srbija.

Asfani, K., Suswanto, H., & Wibawa, A. P. (2016). Influential factors of students’ competence. World Transactions on Engineering and Technology Education, 14(3), 416–420.

Atteberry, A., Loeb S., & Wyckoff, J. (2015). Do first impressions matter? Predicting early career teacher effectiveness. AERA Open, 1(4), 1–23. https://doi.org/10.1177/2332858415607834

Banihabib, M. E., Chitsaz, N., & Randhir, T. O. (2020). Non-compensatory decision model for incorporating the sustainable development criteria in flood risk management plans. SN Applied Sciences, 2(1), 6. https://doi.org/10.1007/s42452-019-1695-6

Barrett, P., Treves, A., Shmis, T., Ambasz, D., & Ustinova, M. (2019). The impact of school infrastructure on learning: A synthesis of the evidence. The World Bank. https://doi.org/10.1596/978-1-4648-1378-8

Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36(4), 1065–1105. https://doi.org/10.1177/0149206309352880

Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218–237. https://doi.org/10.1016/j.compedu.2015.07.003

Brown, J. G. (1971). A note on fuzzy sets. Information and Control, 18(1), 32–39. https://doi.org/10.1016/S0019-9958(71)90288-9

Chang, Y., Leach, N., & Anderman, E. M. (2015). The role of perceived autonomy support in principals’ affective organizational commitment and job satisfaction. Social Psychology of Education, 18(2), 315–336. https://doi.org/10.1007/s11218-014-9289-z

Chen, J., Wang, M., Kirschner, P. A., & Tsai, C. C. (2018). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799–843. https://doi.org/10.3102/0034654318791584

Cheng, Y. C. (1994). Principal’s leadership as a critical factor for school performance: Evidence from multi‐levels of primary schools. School Effectiveness and School Improvement, 5(3), 299–317. https://doi.org/10.1080/0924345940050306

Cheng, Y. C., & Mok, M. M. C. (2007). School‐based management and paradigm shift in education: An empirical study. International Journal of Educational Management, 21(6), 517–542. https://doi.org/10.1108/09513540710780046

Cruickshank, V. (2017). The influence of school leadership on student outcomes. Open Journal of Social Sciences, 5(9), 115–123. https://doi.org/10.4236/jss.2017.59009

Darling-Hammond, L. (2010). Teacher education and the American future. Journal of Teacher Education, 61(1–2), 35–47. https://doi.org/10.1177/0022487109348024

Dihovični, Dj., & Krunić, V. (2018). Creating and encrypting e-commerce database for selling mechanical elements. Applied Engineering Letters, 3(3), 85–89. https://doi.org/10.18485/aeletters.2018.3.3.1

Ďurišová, M., Kucharčíková, A., & Tokarčíková, E. (2015). Assessment of higher education teaching outcomes (Quality of higher education). Procedia-Social and Behavioral Sciences, 174, 2497–2502. https://doi.org/10.1016/j.sbspro.2015.01.922

Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction, 29, 1–9. https://doi.org/10.1016/j.learninstruc.2013.07.001

Gani, A. N., & Assarudeen, S. M. (2012). A new operation on triangular fuzzy number for solving fuzzy linear programming problem. Applied Mathematical Sciences, 6(11), 525–532. https://doi.org/10.13140/2.1.3405.8881

Gerasymchuk, V. H. (2018). Factors of successful management of organization. Tekstilnaindustrija, 66(2), 52–60.

Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18(1), 32–49. https://doi.org/10.1016/j.acme.2017.04.011

Gil-Flores, J., Rodríguez-Santero, J., & Torres-Gordillo, J. J. (2017). Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449. https://doi.org/10.1016/j.chb.2016.11.057

Haapakorpi, A. (2011). Quality assurance processes in Finnish universities: Direct and indirect outcomes and organisational conditions. Quality in Higher Education, 17(1), 69–81. https://doi.org/10.1080/13538322.2011.554311

Hashemkhani Z. S., & Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Inzinerine Ekonomika-Engineering Economics, 24(5), 408–414. https://doi.org/10.5755/j01.ee.24.5.4526

Hashemkhani, Z. S. H., Yazdani, M., & Zavadskas, E. K. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, 22(22), 7399–7405. https://doi.org/10.1007/s00500-018-3092-2

Hitka, M., Kozubikova, L., & Potkany, M. (2017). Education and gender-Based differences in employee motivation. Journal of Business Economics and Management, 19(1), 80–95. https://doi.org/10.3846/16111699.2017.1413009

Jackson, C. K., Rockoff, J. E., & Staiger, D. O. (2014). Teacher effects and teacher-related policies. Annual Review of Economics, 6(1), 801–825. https://doi.org/10.1146/annurev-economics-080213-040845

Jahnke, I., & Liebscher, J. (2020). Three types of integrated course designs for using mobile technologies to support creativity in higher education. Computers & Education, 146, 103782. https://doi.org/10.1016/j.compedu.2019.103782

Jeong, H., Hmelo-Silver, C. E., & Jo, K. (2019). Ten years of computer-supported collaborative learning: A meta-analysis of CSCL in STEM education during 2005–2014. Educational Research Review, 28, 100284. https://doi.org/10.1016/j.edurev.2019.100284

Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12

Klassen, R., & Kim, L. (2019). Selecting teachers and prospective teachers: A meta-analysis. Educational Research Review Journal, 26, 32–51. https://doi.org/10.1016/j.edurev.2018.12.003

Kokeza, G., Urošević S., & Radosavljević, D. (2016). Razvoj kompetencija zaposlenihkao element menadžmentaljudskih resursa – saposebnimosvrtomna tekstilnu industriju. Tekstilna industrija, 64(4), 72–83.

Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105(3), 805. https://doi.org/10.1037/a0032583

Leon, J., Medina-Garrido, E., & Núñez, J. L. (2017). Teaching quality in math class: The development of a scale and the analysis of its relationship with engagement and achievement. Frontiers in Psychology, 8, 895. https://doi.org/10.3389/fpsyg.2017.00895

Lin, J. W., Yen, M. H., Liang, J., Chiu, M. H., & Guo, C. J. (2016). Examining the factors that influence students’ science learning processes and their learning outcomes: 30 years of conceptual change research. Eurasia Journal of Mathematics, Science and Technology Education, 12(9), 2617–2646. https://doi.org/10.12973/eurasia.2016.000600a

Long, C. S., Ibrahim, Z., & Kowang, T. O. (2014). An analysis on the relationship between lecturers’ competencies and students’ satisfaction. International Education Studies, 7(1), 37–46. https://doi.org/10.5539/ies.v7n1p37

Määttä, K., & Uusiautti, S. (2012). How to enhance the smoothness of university students’ study paths. International Journal of Research Studies in Education, 1(1), 47–60. https://doi.org/10.5861/ijrse.2012.v1i1.16

Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M. Z. M., & Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265–292. https://doi.org/10.1016/j.asoc.2017.03.045

Marks, A. B., & Moss, S. A. (2016). What predicts law student success? A longitudinal study correlating law student applicant data and law school outcomes. Journal of Empirical Legal Studies, 13(2), 205–265. https://doi.org/10.1111/jels.12114

Murillo, F. J., & Román, M. (2011). School infrastructure and resources do matter: analysis of the incidence of school resources on the performance of Latin American students. School Effectiveness and School Improvement, 22(1), 29–50. https://doi.org/10.1080/09243453.2010.543538

Narayanan, A. K., & Jinesh, N. (2018). Application of SWARA and TOPSIS methods for supplier selection in a casting unit. International Journal of Engineering Research & Technology, 7(5), 456–458.

Nawi, A., Hamzah, M. I., Ren, C. C., & Tamuri, A. H. (2015). Adoption of mobile technology for teaching preparation in improving teaching quality of teachers. International Journal of Instruction, 8(2), 113–124. https://doi.org/10.12973/iji.2015.829a

Panahi, S., Khakzad, A., & Afzal, P. (2017). Application of stepwise weight assessment ratio analysis (SWARA) for copper prospectivity mapping in the Anarak region, central Iran. Arabian Journal of Geosciences, 10(22), 484. https://doi.org/10.1007/s12517-017-3290-8

Patterson, F., Knight, A., Dowell, J., Nicholson, S., Cousans, F., & Cleland, J. (2016). How effective are selection methods in medical education? A systematic review. Medical Education, 50(1), 36–60. https://doi.org/10.1111/medu.12817

Petrović, G., Mihajlović, J., Ćojbašić, Ž., Madić, M., & Marinković, D. (2019). Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Universitatis, Series: Mechanical Engineering, 17(3), 455–469. https://doi.org/10.22190/FUME190420039P

Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A sistematic review of immersive virtual reality applications forhigher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778

Radionovs, A., & Užga-Rebrovs, O. (2017). Software tool implementing the fuzzy AHP method in ecological risk assessment. Information Technology and Management Science, 20(1), 34–39. https://doi.org/10.1515/itms-2017-0006

Robinson, V. M., Lloyd, C. A., & Rowe, K. J. (2008). The impact of leadership on student outcomes: An analysis of the differential effects of leadership types. Educational Administration Quarterly, 44(5), 635–674. https://doi.org/10.1177/0013161X08321509

Rubio-Alcala, F. D., Arco-Tirado, J. L., Fernandez-Martin, F. D., López-Lechuga, R., Barrios, E., & Pavon-Vazquez, V. (2019). A systematic review on evidences supporting quality indicators of bilingual, plurilingual and multilingual programs in higher education. Educational Research Review, 27, 191–204. https://doi.org/10.1016/j.edurev.2019.03.003

Rulebook on Quality Standards for Institutions. (2012). Official Gazette of the Republic of Serbia, Nos. 7/2011 and 68/2012.

Rulebook on the Evaluation of the Quality of Work of Institutions. (2012). Official Gazette of the Republic of Serbia, No. 9/2012.

Sammons, P., Gu, Q., Day, C., & Ko, J. (2011). Exploring the impact of school leadership on pupil outcomes: Results from a study of academically improved and effective schools in England. International Journal of Educational Management, 25(1), 83–101. https://doi.org/10.1108/09513541111100134

Shahjahan, R. A., & Torres, L. E. (2013). A “global eye” for teaching and learning in higher education: A critical policy analysis of the OECD’s AHELO study. Policy Futures in Education, 11(5), 606–620. https://doi.org/10.2304/pfie.2013.11.5.606

Singh, R., & Sarkar, S. (2015). Does teaching quality matter? Students learning outcome related to teaching quality in public and private primary schools in India. International Journal of Educational Development, 41, 153–163. https://doi.org/10.1016/j.ijedudev.2015.02.009

Smiljanić, V. (2013). Razvojna psihologija. Faculty of Philosophy, Belgrade.

Stahl, G., Koschmann, T., & Suthers, D. D. (2014). Computer-supported collaborative learning. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 479–500). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.029

Teddlie, C., & Reynolds, D. (2000). The international handbook of school effectiveness research. Falmer Press.

Trebješanin, B. (2014). Promene u shvatanjudeteta. Journal of Applied Psychology, 7(4), 549–563. https://doi.org/10.19090/pp.2014.4.549-563

Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 23(2), 107–115. https://doi.org/10.1016/S0261-5177(01)00050-4

Vaiciukevičiute, A., Stankevičiene, J., & Bratčikoviene, N. (2019). Higher education institutions impact on the economy. Journal of Business Economics and Management, 20(3), 507–525. https://doi.org/10.3846/jbem.2019.10156

Vatansever, K., & Akgül, Y. (2019). Multicriteria decision making models for website evaluation. IGI Global. https://doi.org/10.24327/ijcar.2017.3399.0281

Velazquez, M. A., Claudio, D., & Ravindran, A. R. (2010). Experiments in multiple criteria selection problems with multiple decision makers. International Journal of Operational Research, 7(4), 413– 428. https://doi.org/10.1504/IJOR.2010.032419

Vesić, D. (2003). Menadžment ljudskih resursa i kvalitet. Center for Applied Psychology, Belgrade.

Wahyono, I. (2015). Kompetensi Manajerial Kepala Sekolah Dalam Peningkatan Profesionalisme Guru di SMK Bustanul Falah Kembiritan Genteng Banyuwangi. Jurnal Ar-Risalah, 11(1), 50–64. https://doi.org/10.22373/crc.v1i1.306

Won, S., Anderman, E. M., & Zimmerman, R. S. (2020). Longitudinal relations of classroom goal structures to students’ motivation and learning outcomes in health education. Journal of Educational Psychology, 112(5), 1003–1019. https://doi.org/10.1037/edu0000399

Yadegaridehkordi, E., Binti Mohd Noor, N. F., Bin Ayub, M. N., Binti Affal, H., & Binti Hussin, N. (2019). Affective computing in education: A systematic review and future research. Computers & Education, 142, 103649. https://doi.org/10.1016/j.compedu.2019.103649

Yurdakul, M., & İç, Y. T. (2009). Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. Journal of Materials Processing Technology, 209(1), 310–317. https://doi.org/10.1016/j.jmatprotec.2008.02.006

Živković, Ž., & Nikolić, Đ. (2016). Osnove matematičke škole strategijs kogmenadžmenta. Technical Faculty in Bor, Serbia.