Öğrencilerin Uzaktan Öğrenme Ortamında Eğitim Yaşantısının Memnuniyete Etkisi

Öz

Uzaktan eğitimde derslerin niteliğinin değerlendirilmesinde öğrencilerin akademik başarısı dikkat çekerken öğrencilerin duyuşsal özelliklerini ifade eden memnuniyet gibi değişkenlerin de ele alınmasına ihtiyaç duyulmaktadır. Bu araştırmanın amacı, Covid-19 salgın sürecinde acil uzaktan eğitimde öğrenci memnuniyetini yordayan değişkenlerin belirlenmesidir. Araştırmanın amacı doğrultusunda öğrencilerin öğretmen desteği, öğrenci etkileşimi ve iş birliği, kişisel uygunluk, otantik öğrenme, aktif öğrenme ve öğrenci özerkliği değişkenleri temelinde öğrenci memnuniyetinin belirleyicileri araştırılmıştır. Araştırma, 2019-2020 Eğitim Öğretim yılının Bahar Döneminde 1549 ön lisans ve lisans öğrencisiyle yürütülmüştür. Tarama desenlerinden kesitsel tarama modelinin kullanıldığı araştırmada yordayıcı değişkenlerin öğrenci memnuniyetine katkısını belirlemek için regresyon ve CHAID analizi yapılmıştır. Analiz bulgularına göre öğretmen desteği, otantik (gerçekçi) öğrenme, öğrenci iletişimi ve iş birliği ve kişisel uygunluk değişkenlerinin uzaktan eğitimde öğrenci memnuniyetinin birer yordayıcısı olduğu görülmüştür. Bunun yanı sıra araştırma kapsamında bu değişkenlerin öğrenci memnuniyetine etkisi araştırılmıştır. Araştırmanın bu bulgusuna göre öğretmen desteği algısı yüksek olan öğrencilerden öncelikle kişisel uygunluk algısı yüksek ve daha sonra otantik (gerçekçi) öğrenme algısı yüksek olanların en yüksek memnuniyet düzeyine sahip oldukları bulunmuştur. Araştırmanın bulgularının uzaktan öğrenme sürecine ilişkin öğrenci deneyimlerini yansıtması açısından yapılacak çalışmalara temel oluşturması öngörülmektedir. Ayrıca araştırma bulgularının uzaktan eğitimin niteliğini arttırmaya yönelik kurumlara yol göstereceği düşünülmektedir.

Anahtar Kelimeler

• Uzaktan eğitim
• öğrenci memnuniyeti
• uzaktan öğrenme ortamında eğitim yaşantısı
• regresyon analizi
• CHAID analizi

The Effect of Students' Education Experience on Satisfaction in Distance Learning Environment

Öz

In evaluating the quality of the courses in distance education, while the students' academic success draws attention, it is necessary to consider the variables such as satisfaction, which expresses the students' affective characteristics. This research aims to determine the educational experiences in the distance learning environment that affect students' satisfaction with emergency distance education during the Covid-19 pandemic. In line with the research purpose, the determinants of student satisfaction regarding instructor support, student interaction and collaboration, personal relevance, authentic learning, active learning, and student autonomy were investigated. The research was conducted with 1549 associate and undergraduate students in the spring term of the 2019-2020 academic year. Regression and CHAID analysis were performed to determine the contribution of the predictor variables to student satisfaction. According to the analysis findings, it was seen that instructor support, authentic learning, student interaction and collaboration, and personal relevance were predictors of student satisfaction in distance education. In addition, the effects of these variables on student satisfaction were investigated within the scope of the research. According to the this finding of the study, it was found that among the students with a high perception of teacher support, those with a heightened perception of personal relevance and a high perception of authentic learning had the highest level of satisfaction. It is foreseen that the study's findings will form the basis for the studies to reflect the student experiences regarding the distance learning process. In addition, it is thought that the research findings will guide the institutions to increase the quality of distance education.

Anahtar Kelimeler

• Distance education
• student satisfaction
• DELES
• regression analysis
• CHAID analysis

Kaynakça

1. Abdous, M., & Yen, C. J. (2010). A predictive study of learner satisfaction and outcomes in face-to-face, satellite broadcast, and live video-streaming learning environments. The Internet and Higher Education, 13(4), 248–257. https://doi.org/10.1016/j.iheduc.2010.04.005.
2. Alan, Y., Biçer, N., & Can, F. (2020). Perspectives of pre-service teachers on distance education: Covid-19 process. Revista Argentina de Clínica Psicológica, 29(5), 1972-1984. https://doi.org/10.24205/03276716.2020.1193 .
3. Allen, M., Bourhis, J., Burrell, N., & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta-analysis. American Journal of Distance Education, 16(2), 83–97. https://doi.org/10.1207/s15389286ajde1602_3
4. Almarashdeh, I. (2016). Sharing instructors experience of learning management system: A technology perspective of user satisfaction in distance learning course. Computers in Human Behavior, 63, 249–255. https://doi.org/10.1016/j.chb.2016.05.013
5. Alqurashi, E. (2018). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562.
6. Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148. https://doi.org/10.1080/01587919.2018.1553562.
7. Asio, J. M. R., & Bayucca, S. A. (2021). Spearheading education during the COVID-19 rife: Administrators level of digital competence and schools readiness on distance learning. Journal of Pedagogical Sociology and Psychology, 3(1), 19-26. https://doi.org/10.33902/JPSP.2021364728.
8. Barnard, L., Paton, V., & Lan, W. (2008). Online self-regulatory learning behaviors as a mediator in the relationship between online course perceptions with achievement. The International Review of Research in Open and Distributed Learning, 9(2). https://doi.org/10.19173/irrodl.v9i2.516

9. Bolliger, D. U., & Martindale, T. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-Learning, 3(1), 61–67. https://www.learntechlib.org/primary/p/2226/
10. Bossman, A., & Agyei, S. K. (2022). Technology and instructor dimensions, e-learning satisfaction, and academic performance of distance students in Ghana. Heliyon, 8(4), e09200. https://doi.org/10.1016/j.heliyon.2022.e09200
11. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2014). Bilimsel araştırma yöntemleri (17. Baskı). Ankara: Pegem Yayınları.
12. Chae, S. E., & Shin, J.-H. (2015). Tutoring styles that encourage learner satisfaction, academic engagement, and achievement in an online environment. Interactive Learning Environments, 24(6), 1371–1385. https://doi.org/10.1080/10494820.2015.1009472
13. Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). Routledge.
14. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage.
15. Demir, B., & Walker, S. L. (2022). Psychosocial learning environments: EFL learners’ voices. English Language Teaching Educational Journal, 5(1), 16-31. https://doi.org/10.12928/eltej.v5i1.5770.
16. Díaz-Pérez, F. M., & Bethencourt-Cejas, M. (2016). CHAID algorithm as an appropriate analytical method for tourism market segmentation. Journal of Destination Marketing & Management, 5(3), 275–282. https://doi.org/10.1016/j.jdmm.2016.01.006
17. Durak, G., Çankaya, S., & İzmirli, S. (2020). COVID-19 pandemi döneminde Türkiye’deki üniversitelerin uzaktan eğitim sistemlerinin incelenmesi. Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi, 14(1), 787–810. https://doi.org/10.17522/balikesirnef.743080
18. Dziuban, C., Moskal, P., Thompson, J., Kramer, L., DeCantis, G., & Hermsdorfer, A. (2015). Student satisfaction with online learning: Is it a psychological contract? Online Learning, 19(2). https://doi.org/10.24059/olj.v19i2.496
19. Ferrer-Cascales, R., Walker, S. L., Reig-Ferrer, A., Fernández-Pascual, M. D., & Albaladejo-Blázquez, N. (2011). Evaluation of hybrid and distance education learning environments in Spain. Australasian Journal of Educational Technology, 27(7). https://doi.org/10.14742/ajet.906
20. Fraenkel, J. R., Wallen, N. E., & Hyun, H. (2012). How to design and evaluate research in education. Mcgraw-Hill.
21. Forson, I. K., & Vuopala, E. (2019). Online learning readiness: Perspective of students enrolled in distance education in Ghana. The Online Journal of Distance Education and E-Learning, 7(4), 277-294. http://www.tojdel.net/journals/tojdel/articles/v07i04/v07i04-03.pdf
22. Hagedorn, R. L., Wattick, R. A., & Olfert, M. D. (2022). “My Entire World Stopped”: College Students’ Psychosocial and Academic Frustrations during the COVID-19 Pandemic. Applied Research in Quality of Life, 17(2), 1069-1090. https://doi.org/10.1007/s11482-021-09948-0
23. Harsasi, M., & Sutawijaya, A. (2018). Determinants of student satisfaction in online tutorial: A study of a distance education institution. Turkish Online Journal of Distance Education, 19(1), 89–99. https://doi.org/10.17718/tojde.382732
24. Hebebci, M. T., Bertiz, Y., & Alan, S. (2020). Investigation of Views of Students and Teachers on Distance Education Practices during the Coronavirus (COVID-19) Pandemic. International Journal of Technology in Education and Science, 4(4), 267-282. https://doi.org/10.46328/ijtes.v4i4.113
25. Huck, S. W. (2012). Reading statistics and research. Pearson Education, Repr.
26. İncebacak, B. B. (2022). Pandemi döneminde çevirimiçi yaratici drama. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 24(2), 218–230. https://doi.org/10.17556/erziefd.992687
27. Joo, Y. J., Joung, S., & Son, H. S. (2014). Structural relationships among effective factors on e-learners' motivation for skill transfer. Computers in Human Behavior, 32, 335–342. https://doi.org/10.1016/j.chb.2013.08.011.
28. Jung, I., Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education & Teaching International, 39(2), 153-162. https://doi.org/10.1080/14703290252934603
29. Karasar, N. (2003). Bilimsel Araştırma Yöntemi. Ankara: Nobel Yayın Dağıtım.
30. Kaminski, K., Switzer, J., & Gloeckner, G. (2009). Workforce readiness: A study of university students’ fluency with information technology. Computers & Education, 53(2), 228-233. https://doi.org/10.1016/j.compedu.2009.01.017
31. Kaufmann, R., & Vallade, J. I. (2022). Exploring connections in the online learning environment: Student perceptions of rapport, climate, and loneliness. Interactive Learning Environments, 30(10), 1794-1808. https://doi.org/10.1080/10494820.2020.1749670
32. Keskin, S., & Yurdugül, H. (2020). Kestirim Yöntemleri. Eğitsel Veri Madenciliği ve Öğrenme Analitikleri İçinde (ss. 66-88). Ankara: Anı Yayıncılık.
33. Kırmızı, Ö. (2015). The influence of learner readiness on student satisfaction and academic achievement in an online program at higher education. The Turkish Online Journal of Educational Technology, 14(1). https://files.eric.ed.gov/fulltext/EJ1057353.pdf
34. Kılıç, S. (2013). Sampling methods. Journal of Mood Disorders, 3(1), 44. https://doi.org/10.5455/jmood.20130325011730
35. Koğar, E. Y. (2019). The Investigation of the relationship between mathematics and science literacy and information and communication technology variables. International Electronic Journal of Elementary Education, 11(3), 257–271. https://www.iejee.com/index.php/IEJEE/article/view/710/389
36. Kuo, Y.-C., 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. https://doi.org/10.19173/irrodl.v14i1.1338
37. Larson, D. K., & Sung, C.-H. (2009). Comparing student performance: Online versus blended versus face-to-face. Journal of Asynchronous Learning Networks, 13(1), 31–42. https://eric.ed.gov/?id=EJ837556
38. Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277–283. https://doi.org/10.1016/j.iheduc.2010.08.002
39. Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24. https://doi.org/10.1016/j.compedu.2012.07.015
40. Marks, R., Sibley, S., & Arbaugh, J. B. (2005). A structural equation model on predictors for effective online learning. Journal of Management Education, 29(4) 531-563. https://doi.org/10.1177/1052562904271199
41. Milanović, M., & Stamenković, M. (2016). CHAID decision tree: Methodological frame and application. Economic Themes, 54(4), 563–586. https://doi.org/10.1515/ethemes-2016-0029
42. Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/08923648909526659
43. Muzammil, Moh., Sutawijaya, A., & Harsasi, M. (2020). Investigating student satisfaction in online learning: The role of student interaction and engagement in distance learning university. Turkish Online Journal of Distance Education, 21(Special Issue-IODL), 88–96. https://doi.org/10.17718/tojde.770928
44. Orhan Göksün, D. (2019). Predictors of perceived learning in a distance learning environment from the perspective of SIPS model. International Journal of Human–Computer Interaction, 36 (10). 941-952. https://doi.org/10.1080/10447318.2019.1700643
45. Özkök, A., Walker, S. L., & Büyüköztürk, Ş. (2009). Reliability and validity of a Turkish version of the DELES. Learning Environments Research, 12(3), 175–190. https://doi.org/10.1007/s10984-009-9060-0
46. Özkök, G. A., & Tütüncü, H. (2020). Harmanlanmış e-öğrenme ortamı bilgi sistem kalitesinin öğrencilerin psikososyal algılarına etkisinin incelenmesi. Mediterranean Journal of Educational Research, 14(33), 1–24. https://doi.org/10.29329/mjer.2020.272.1
47. Özkök, G. A., & Tütüncü, H. (2022). İki yükseköğretim kurumunun harmanlanmış öğrenme ortamlarının psikososyal boyutlarının karşılaştırmalı olarak incelenmesi. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi, 23(1), 923-953. https://dergipark.org.tr/tr/pub/kefad/issue/69091/764064
48. Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS. Allen & Unwin.
49. Rajabalee, Y. B., & Santally, M. I. (2020). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26. 2623–2656. https://doi.org/10.1007/s10639-020-10375-1
50. Şahin, İ. (2007). Predicting student satisfaction in distance education and learning environments. Turkish Online Journal of Distance Education, 8(2), 113–119. https://dergipark.org.tr/tr/pub/tojde/issue/16920/176580
51. Silahtaroğlu, G. (2011). Veri Madenciliği: Kavram ve Algoritmalar (2nd ed.). İstanbul: Papatya Yayıncılık.
52. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson Education.
53. Walker, S. L., & Fraser, B. J. (2005). Development and validation of an instrument for assessing distance education learning environments in higher education: The distance education learning environments survey (DELES). Learning Environments Research, 8(3), 289–308. https://doi.org/10.1007/s10984-005-1568-3
54. Waterhouse, P., Samra, R., & Lucassen, M. (2022). Distance education students' satisfaction: Do work and family roles matter? Distance Education, 43(1), 56–77. https://doi.org/10.1080/01587919.2021.2020622
55. Wei, H.-C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48–69. https://doi.org/10.1080/01587919.2020.1724768
56. Yükseltürk, E., & Yıldırım, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students' satisfaction in an online certificate program. Journal of Educational Technology & Society, 11(4), 51–65. https://www.jstor.org/stable/jeductechsoci.11.4.51
57. Żogała-Siudem, B., & Jaroszewicz, S. (2021). Fast stepwise regression based on multidimensional indexes. Information Sciences, 549, 288–309. https://doi.org/10.1016/j.ins.2020.11.031