Examination of student satisfaction with e-courses by clustering analysis





e-courses, students’ satisfaction, clustering analysis, online learning environment


Student satisfaction is one of the foremost factors for e-courses to continue efficiently and achieve their goals. The present research aims to analyze the clustering tendencies of e-course students’ satisfaction with e-courses according to the variables of “gender, class, computer ownership, computer and internet literacy, duration of internet use, the connection type to the internet, and the tools utilized by the students in the learning management system” through clustering algorithms, one of the data mining techniques. The survey model was employed. A 35-item satisfaction scale devised for students taking e-courses was used for data collection. A total of 522 students taking online courses at a university participated in the study. Descriptive statistics and cluster analysis were employed to analyze and interpret the data. As a result of the analyses through K-Means cluster analysis, four different clusters were obtained. Those clusters were defined in accordance with the variables they included as follows: Cluster 1 (the group with the highest rate of computer ownership and the highest computer literacy); Cluster 2 (the group with the lowest rate of computer ownership and the lowest computer literacy); Cluster 3 (students have low computer literacy and low duration of Internet use), and Cluster 4 (the group with the highest rate of females, first graders, and those who use the Internet at “good” and “very good” levels) . According to the research results, the mean student satisfaction levels regarding e-courses were determined to be low in general. As per the clustering analysis, it was observed that the students in Cluster 1 and Cluster 4 had the highest mean values of satisfaction with e-courses and a “good” level of satisfaction compared to the other groups, while Cluster 2 had the lowest mean value. Cluster 3, on the other hand,  was the one with a “medium” level of satisfaction.


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Agyapong, K. (2021). Students’ perspectives on satisfaction with distance education in Ghana: A cluster analysis. African Journal of Teacher Education, 10, 365–391. https://doi.org/10.21083/ajote.v10i1.6287

Antonenko, P.D., Toy, S., & Niederhauser, D.S. (2012). Using cluster analysis for data mining in educational technology research. Education Technology Research Development 60, 383–398. https://doi.org/10.1007/s11423-012-9235-8

Bautista, E., Quintana, N. L., & Sánchez, O. A. (2022). Distance education with university students quarantined by COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 5-13. https://doi.org/10.24310/innoeduca.2022.v8i2.12257

Bollinger, D.U., & Erichsen, E.A. (2013). Student satisfaction with blended and online courses based on personality type. Canadian Journal of Learning & Technology, 39(1), 1-23. https://doi.org/10.21432/T2B88W

Bolliger, D. U., Supanakorn, S. & Boogs, C. (2010). Impact of podcasting on student motivation in the online learning environment. Computers & Education, 55(2), 714- 722. https://doi.org/10.1016/j.compedu.2010.03.004

Bossman, A., & Agyei, S.K. (2022). Technology and instructor dimensions, e-learning satisfaction, and academic performance of distance students in Ghana. Heliyon, 8(4), 1-16. https://doi.org/10.1016/j.heliyon.2022.e09200.

Cakır, O. , Karademir, T., & Erdoğdu, F. (2018). Psychological variables of estimating distance learners' motivation. Turkish Online Journal of Distance Education, 19(1), 163-182. https://doi.org/10.17718/tojde.382795

Chen, N. S., Lin, K. M., & Kinshuk (2008). Analyzing users’ satisfaction with e-learning using a negative critical incidents approach. Innovations in Education and Teaching International, 45(2), 115-126. https://doi.org/10.1080/14703290801950286

Chua, C., & Montalbo, J. (2014). Assessing students’ satisfaction on the use of virtual learning environment (VLE): An input to a campus-wide e-learning design and implementation. Information and Knowledge Management, 3(4), 108-116.

Çetintürk, I., & Gençtürk, M. (2020). The classification of the health expenditure indicators of OECD countries through clustering analysis. Süleyman Demirel University Visionary Journal, 11(26) , 228-244 . https://doi.org/10.21076/vizyoner.650681

Donavant, B. W. (2009). The new, modern practice of adult education: online instruction in a continuing professional education setting. Adult Education Quarterly, 59(3), 227-245. https://doi.org/10.1177/0741713609331546

Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ Experiences, Learning Outcomes and Satisfaction in e-Learning. Journal of e-Learning and Knowledge Society, 13(2). https://doi.org/10.20368/1971-8829/144

Gülbahar, Y. (2012). Study of developing scales for assessment of the levels of readiness and satisfaction of participants in e-learning environments. Ankara University, Journal of Faculty of Educational Sciences, 45(2), 119-137. https://doi.org/10.1501/Egifak_0000001256

Harsasi, M., & Sutawijaya, A. (2018). Determinants of student satisfaction in online tutorial: A study of a distance education ınstitution. Turkish Online Journal of Distance Education, 19(1), 89-99. https://doi.org/10.17718/tojde.382732

Ilgaz, H. (2008). The contribution of technology acceptance and the sense of being community to learner satisfaction in distant education [Thesis doctoral, Hacettepe University, Ankara]. Institute of Science, Ankara.

Karabatak, S. Alanoglu, M., & Karabatak, M.(2020, June 1-2). Effects of homework supported distance education on academic satisfaction, academic achievement, and attitude towards distance education [Conference paper]. 8th International Symposium on Digital Forensics and Security (ISDFS), Beirut, Lebanon. https://doi.org/10.1109/ISDFS49300.2020.9116372

Karasar, N. (2006). Scientific Research Method. Nobel Publication.

Kaya, M. (2022). Attitudes of higher education students about the use of distance education environments during the covid-19 outbreak process. Fırat Üniversitesi Sosyal Bilimler Dergisi, 32(3), 995-1011. https://doi.org/10.18069/firatsbed.1103453

Kolburan, A., & Deveci, A. (2015). Development of satisfaction scale for e-course: Reliability and validity study. Journal of Theory & Practice in Education, 11(4), 1272-1287.

Kuo, Y., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16–39. https://doi.org/10. 19173/irrodl.v14i1.1338.

Kuo, Y., Walker, A.E., Schroder, K.E.E., & Belland, B.R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001

Lidegran, I., Hultqvist, E., Bertilsson, E., & Börjesson, M.(2021). Insecurity, lack of support, and frustration: a sociological analysis of how three groups of students reflect on their distance education during the pandemic in Sweden. European Journal of Education, 56(4), 550-563. https://doi.org/10.1111/ejed.12477

Martín, O., Fernández, J. C., Montero, M. A, & González, F. (2015). The main components of satisfaction with e-learning. Technology, Pedagogy and Education, 24(2), 267-277. https://doi.org/10.1080/1475939X.2014.888370

Moore, C., & Moore, J. (2005). The Sloan Consortium Quality Framework and the Five Pillars. The Sloan Consortium.

Nortvig, A.M., Petersen, A.K., & Balle, S.H. (2018). A Literature Review of the Factors Influencing E Learning and Blended Learning in Relation to Learning Outcome, Student Satisfaction and Engagement. The Electronic Journal of e-Learning (EJEL), 16(1), 46-55.

Terzi̇, Ö., Küçüksi̇lle, E., Ergi̇n, G., & Ilker, A. (2011). Estimation of solar radiation using data mining process. International Journal of Technological Sciences, 3(2), 29-37.

Unsihuay, J. E. G., & Blanco, A. Z. (2021). Modelos de minería de datos aplicados al rendimiento académico universitario: Educación virtual durante pandemia COVID-19. Tierra Nuestra, 15(1), 18-28. http://dx.doi.org/10.21704/.rtnv15i1.1812

Vásquez, M.-S., Nuñez, P., & Cuestas, J. (2023). Teachers’ Digital Competences in the context of COVID-19. A quantitative approach. Pixel-Bit. Revista De Medios Y Educación, (67), 155–185. https://doi.org/10.12795/pixelbit.98129

Yildirim, Ö. (2020). Canli ders öncesi çevresel düzenlemeler ve ortam hazirliği. In S. Karaman, E. Kurşun (Ed.), Uzaktan öğretimde canli ders uygulama ilkeleri ve örnekleri (p. 24-38). Atatürk Üniversitesi Yayinlari.

Zhou, G., Yang, L., Liu, W., Shi, J., & Shen, J. (2022). Exploratory research on satisfaction degree in distance education. Applied Sciences, 12(15), e7889. https://doi.org/10.3390/app12157889




How to Cite

Deveci Topal, A., & Kolburan Geçer, A. (2023). Examination of student satisfaction with e-courses by clustering analysis. Innoeduca. International Journal of Technology and Educational Innovation, 9(2), 39–50. https://doi.org/10.24310/innoeduca.2023.v9i2.16681