https://aboutmusicschools.com https://slotmgc.com https://300thcombatengineersinwwii.com https://mobilephonesource.co.uk https://discord-servers.io https://esmark.net https://slotmgc.com https://nikeshoesinc.us https://ellisislandimmigrants.org https://holidaysanthology.com https://southaventownecenter.net https://jimgodfreydesign.com https://mckinneypaintingpros.com https://enchantedmansion.org https://mckinneypaintingpros.com https://laurabrodieauthor.com https://holidaysanthology.com https://ardictionary.com https://113.30.151.116 https://103.252.118.20 https://206.189.83.174 https://157.230.39.109 https://128.199.85.208 https://172.104.51.149 https://174.138.21.250 https://157.245.50.183 https://152.42.239.189 https://188.166.210.125 https://152.42.178.155 https://192.53.172.202 https://172.104.188.91 https://103.252.118.157 https://63.250.61.107 https://165.22.104.74

Analisis Faktor-Faktor yang Mempengaruhi Prestasi Siswa Menggunakan Metode Structural Equation Modeling (SEM)

Authors

  • Aplonia Dima Program Studi Matematika, Universitas Nusa Cendana Kupang
  • Maria A. Kleden Program Studi Matematika, Universitas Nusa Cendana Kupang
  • Astri Atti Program Studi Matematika, Universitas Nusa Cendana Kupang

DOI:

https://doi.org/10.29313/statistika.v23i2.2642

Keywords:

Prestasi Belajar, Structural Equation Modeling, Learning Achievement

Abstract

ABSTRAK

Prestasi akademik siswa dipengaruhi oleh sejumlah variabel internal dan eksternal. Tujuan dari studi ini adalah untuk mengisolasi variabel paling penting yang terkait dengan kinerja akademik dengan menggunakan Structural Equation Modeling (SEM). Lingkungan, instruktur, dan motivasi siswa semuanya berperan penting. Sebanyak 203 siswa dari kelas 2018, 2019, dan 2020 yang terdaftar di Program Studi Matematika FST UNDANA termasuk dalam demografi studi ini. Dengan menggunakan metode proportional random sampling, terpilih 100 orang responden untuk diteliti. Sebuah kuesioner digunakan untuk mengumpulkan informasi dan telah diuji untuk validitas dan reliabilitas. Informasi tersebut dianalisis menggunakan Structural Equation Modeling (SEM). Hasil penelitian menunjukkan bahwa model SEM dari faktor-faktor yang memengaruhi prestasi akademik siswa direpresentasikan oleh persamaan Y=0.9878X1+0.685X2+1.159X3.

ABSTRACT

The academic achievement of students is influenced by a number of internal and external variables. The purpose of this study is to isolate the most important variables related to academic performance using Structural Equation Modelling (SEM). The environment, instructors, and student motivation all play crucial roles.  A total of 203 students from the 2018, 2019, and 2020 classes enrolled in the Mathematics Study Program at the Faculty of Science and Technology (FST) UNDANA are included in the demographic of this study. Using the proportional random sampling method, 100 respondents were selected for investigation. A questionnaire was employed to collect information and has been tested for validity and reliability. The collected information was analyzed using Structural Equation Modelling (SEM). The research results indicate that the SEM model of factors influencing students' academic achievement is represented by the equation Y=0.9878X1+0.685X2+1.159X3.

References

Altbach, P. G. (2013). Advancing the national and global knowledge economy: The role of research universities in developing countries. Studies in Higher Education, 38(3). https://doi.org/10.1080/03075079.2013.773222.

Altbach, P. G. (2013). Advancing the national and global knowledge economy: The role of research universities in developing countries. Studies in Higher Education, 38(3). https://doi.org/10.1080/03075079.2013.773222.

Baltabayeva, M., & Kodirova, D. (2022). The need to provide the priority of spiritual and educational processes in the modern education system. ACADEMICIA: An International Multidisciplinary Research Journal, 12(1). https://doi.org/10.5958/2249-7137.2022.00069.6.

Boeren, E. (2019). Understanding Sustainable Development Goal (SDG) 4 on “quality education” from micro, meso and macro perspectives. International Review of Education, 65(2). https://doi.org/10.1007/s11159-019-09772-7.

Bradley, R. H., & Putnick, D. L. (2012). Housing quality and access to material and learning resources within the home environment in developing countries. Child Development, 83(1). https://doi.org/10.1111/j.1467-8624.2011.01674.x.

Cebrián, G., Junyent, M., & Mulà, I. (2020). Competencies in education for sustainable development: Emerging teaching and research developments. In Sustainability (Switzerland) (Vol. 12, Issue 2). https://doi.org/10.3390/su12020579.

Ćukušić, M., Garača, Ž., & Jadrić, M. (2014). Online self-assessment and students’ success in higher education institutions. Computers and Education, 72. https://doi.org/10.1016/j.compedu.2013.10.018.

De Carvalho, J., & Chima, F. O. (2014). Applications of structural equation modeling in social sciences research. American International Journal of Contemporary Research, 4(1), 6-11.

Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of research in personality, 19(2), 109-134.

Ferdinand. (2002). Structural Equation Modelling dalam Penelitian Manajemen: Aplikasi Model-

Ghozali, D. I. (2012). Analisis Faktor-Faktor yang Mempengaruhi Prestasi Mahasiswa dalam Mempelajari Mata Kuliah Akuntansi Keuangan Menengah (Studi empiris pada Mahasiswa Jurusan Akuntansi reguler di Fakultas Ekonomika dan Bisnis Universitas Dipenogoro tahun angkatan 09 dan 10. Diponegoro Journal of Accounting, 1-13.

Gil, P. D., da Cruz Martins, S., Moro, S., & Costa, J. M. (2021). A data-driven approach to predict first-year students’ academic success in higher education institutions. Education and Information Technologies, 26(2). https://doi.org/10.1007/s10639-020-10346-6.

Glewwe, P., Siameh, C., Sun, B., & Wisniewski, S. (2021). School resources and educational outcomes in developing countries. In The Routledge Handbook of the Economics of Education. https://doi.org/10.4324/9780429202520-10.

Hattie, J. (2009). The black box of tertiary assessment: An impending revolution. Tertiary assessment & higher education student outcomes: Policy, practice & research, 259, 275.

Hossain, M. Al, Hossen, S., & Akhter, J. (2018). Quantifying diversity and composition of tree species in Satchari National Park. Int. J. of Usuf. Mngt., 19(November 2018).

Iglesias-Pradas, S., Hernández-García, Á., Chaparro-Peláez, J., & Prieto, J. L. (2021). Emergency remote teaching and students’ academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119. https://doi.org/10.1016/j.chb.2021.106713

Influence, T. H. E., Transformational, O. F., Discipline, W., The, O. N., Performance, W., & Employees, E. S. (2021). THE INFLUENCE OF TRANSFORMATIONAL LEADERSHIP AND WORK DISCIPLINE ON THE WORK PERFORMANCE OF EDUCATION SERVICE EMPLOYEES. Multicultural Education, Vol. 08, N.

Khan, P. A., Johl, S. K., Akhtar, S., Asif, M., Salameh, A. A., & Kanesan, T. (2022). Open Innovation of Institutional Investors and Higher Education System in Creating Open Approach for SDG-4 Quality Education: A Conceptual Review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1). https://doi.org/10.3390/joitmc8010049.

Kline. (1998). Principles and Practice of Structural Equation Modelling. New York: Guildford Press.

Kuchai, O., Skyba, K., Demchenko, A., & Savchenko, N. (2022). The Importance of Multimedia Education in the Informatization of Society. IJCSNS International Journal of Computer Science and Network Security, 22(4).

Kumar, S., Gupta, S. K., & Rawat, M. (2019). Resources and utilization of geothermal energy in India: An Eco - friendly approach towards sustainability. In Materials Today: Proceedings (Vol. 26, pp. 1660–1665). Elsevier. https://doi.org/10.1016/j.matpr.2020.02.347.

Mansolf, M., Jorgensen, T. D., & Enders, C. K. (2020). A multiple imputation score test for model modification in structural equation models. Psychological methods, 25(4), 393.

Marsh, H. W., & Roche, L. A. (2000). Effects of grading leniency and low workload on students' evaluations of teaching: Popular myth, bias, validity, or innocent bystanders?. Journal of educational psychology, 92(1), 202.

model Rumit. Semarang: Badan Penerbit Universitas Diponegoro.

Ng, C. F. (2021). The physical learning environment of online distance learners in higher education–a conceptual model. Frontiers in Psychology, 12, 635117.

Nousheen, A., Yousuf Zai, S. A., Waseem, M., & Khan, S. A. (2020). Education for sustainable development (ESD): Effects of sustainability education on pre-service teachers’ attitude towards sustainable development (SD). Journal of Cleaner Production, 250. https://doi.org/10.1016/j.jclepro.2019.119537.

Nurjan. (2015). Psikologi Belajar. Ponorogo: CV. Wade Group.

Opstoel, K., Chapelle, L., Prins, F. J., De Meester, A., Haerens, L., van Tartwijk, J., & De Martelaer, K. (2020). Personal and social development in physical education and sports: A review study. European Physical Education Review, 26(4). https://doi.org/10.1177/1356336X19882054.

Oyelaran-Oyeyinka, B., & Lal, K. (2006). Learning new technologies by small and medium enterprises in developing countries. Technovation, 26(2). https://doi.org/10.1016/j.technovation.2004.07.015.

Pickett, L., & Fraser, B. (2010). Creating and assessing positive classroom learning environments. Childhood Education, 86(5), 321-326.

Santoso, S. (2018). Analisis SEM Menggunakan AMOS. PT. Elex Media Komputindo.

Smith, T. D., & McMillan, B. F. (2001). A Primer of Model Fit Indices in Structural Equation Modeling.

Vadakalu Elumalai, K., P Sankar, J., R, K., Ann John, J., Menon, N., Salem M Alqahtani, M., & Abdulaziz Abumelha, M. (2022). Factors Affecting the Quality of E-Learning During the COVID-19 Pandemic From the Perspective of Higher Education Students. Proceedings of the 2022 InSITE Conference. https://doi.org/10.28945/4909.

Wang, M. T., & Eccles, J. S. (2013). School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective. Learning and Instruction, 28, 12-23.

Whittaker, T. A. (2012). Using the modification index and standardized expected parameter change for model modification. The Journal of Experimental Education, 80(1), 26-44.

Yakubu, M. N., & Abubakar, A. M. (2022). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, 51(2). https://doi.org/10.1108/K-12-2020-0865

Zeegers, P. (2004). Student learning in higher education: A path analysis of academic achievement in science. Higher Education Research and Development, 23(1). https://doi.org/10.1080/0729436032000168487.

Downloads

Published

2023-11-30

Issue

Section

Articles