Psychometric Properties of the Persian Version of Learner Satisfaction Survey in Online Courses: A Transcultural Adaptation and Psychometric Study

Document Type : Original Article

Authors

1 Department of Education , Farhangian University, Tehran, Iran.

2 Department of Education, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

3 Department of Educational Technology, Faculty of Education and Psychology, Allameh Tabatab’i University, Tehran, Iran

Abstract

Background: Following the importance of assessing the online courses from Iranian learner’s viewpoint, this study aimed to examine the psychometric properties of the Learner Satisfaction Survey (LSS) in online courses in the Iranian Methods: In this transcultural adaptation and psychometric study, 526 students who had studied at least 2 semesters online were selected based on convenience sampling method and enrolled in eight public universities of Iran (Based on geographical distribution) from October 2022 to January 2023. The learner satisfaction survey (Chang, 2013), a short version of the Online Satisfaction Survey (Strachota,2003), was used to evaluate the learners’ satisfaction with online courses. It comprises 25 self-report items measuring four types of interaction (learner–content, learner– instructor, learner–learner, and learner–technology interactions) and a general satisfaction. The tool was translated using standard forward–backward technique. Second, psychometric properties, including the face, content, construct, convergent and divergent validities, and reliability were examined using Cronbach’s alpha and McDonald’s omega coefficients.
Results: Linguistic and conceptual equivalence of the translated questionnaire was higher than 1.5 and content validities values (CVR>0.78 & CVI>0.79) were calculated at an acceptable level according to experts. None of 25 items was removed based on face and content validity coefficients. The exploratory factor analysis showed that five factors were identified that predicted 73.723% of the total variance. A factors structure with adequate fit indices (Χ2/df=2.23<3 ،RMSEA=0.069، GFI=0.96 ،NFI=0.97 ،CFI=0.98, RMR=0.045, SRMR=0.047) based on five secondorder factors (learner–content, learner–instructor, learner–learner, and learner–technology interactions, and general satisfaction) and one second-order factor (total score for academic satisfaction) were confirmed. Reliability of LSS was satisfactory, with McDonald’s omega ranging from 0.880 to 0.955 and Cronbach alpha ranging from 0.881 to 0. 955. The findings confirmed the convergent validity (AVE>0.5 and CR>AVE) for each subscale and total scale. Also, ASV<AVE& MSV<AVE pconfirmed the divergent validity for each subscale and total scale.
Conclusion: The results indicate that the LSS has acceptable psychometric properties and can be considered as a suitable tool for measuring satisfaction with online courses in the Iranian context.

Keywords

References

Ulum H. The effects of online education on academic success: A meta-analysis study.  Educ  Inf  Technol  (Dordr). 2022;27(1):429 - 450. doi: 10.10 07/s10639 - 021-10740-8. Epub 2021 Sep 6. PMID: 34512101; PMCID: PMC8419824.
Basith A, Rosmaiyadi R, Triani SN, Fitri F. Investigation of online learning satisfaction during COVID 19: In relation to academic achievement. J Educ Sci Technol (EST) [Internet]. 2020;1(1):265–
75. doi:10.26858/est.v1i1.14803.
Barrutia, I., et al. “Qualitative analysis of the level of satisfaction with virtual education in university students in times of pandemic.” NTQR 7 (2021): 220-228. ‏
Moore JC, Shelton K. The Sloan Consortium pillars and quality scorecard. I n: Shat t uck K , ed itor. Assu r i ng Q u alit y i n O n li ne Educat ion: P r act ices a nd P rocesses at the Teaching, Resource, and Program Levels. Sterling, VA: Stylus Publishing; 2014. p. 40–9.
Meyer K A. St udent engagement i n on li ne learning: What works and why: Student engagement online. ASHE High Educ Rep [Internet]. 2014;40(6):1–114. Available from: ht t p://d x.doi.org /10.10 02/a ehe.20 018                              Croxton  RA  (2014).  The  role  of interactivity in student satisfaction and persistence in online learning. J Online Learn Teach.;10(2):314. 
Moore MG. Editorial: Three types of interaction. Am J Distance Educ [Internet]. 1989;3(2):1–7. Available from: ht t p:// dx.doi.org/10.1080/08923648909526659
Palloff RM, Pratt K. Lessons From the Cyberspace Classroom- The Realities of Online Teaching. San Francisco, CA: Jossey-Bass; 2001.
Strachota EM. Student Satisfaction in Online Courses: An Analysis of the Impact of Learner-Content, Learner- Instructor, Learner-Learner, and Learner- Technology Interaction. Milwaukee (EE. UU; 2003.
Driver M. Exploring student perceptions of group interaction and class satisfaction in the web-enhanced classroom. Internet High Educ [Internet]. 2002;5(1):35–45. Available from: ht t p://d x.doi.org /10.1016/ s1096 -7516(01) 0 0 076 - 8
Frey BA, Alman SW. Applying adult learning theory to the online classroom. New Horiz Adult Educ Hum Resour Dev [Internet]. 2003;17(1):4–12. Available from: ht t p://d x.doi.org /10.10 02/n ha 3.10155
Finlay W, Desmet C, Evans L. Is it the technology or the teacher? A comparison of  online  and  traditional  English Composition classes. J Educ Comput Res  [Internet].  2004;31(2):163–80. Available from: http://dx.doi.org/10.2190/ urjj-hxha-ja08-5lvl
Chang S -H H, Sm ith R A. Ef fect iveness of personal interaction in a learner-centered paradigm distance education class based on student satisfaction. J Res Technol Educ  [Internet].  2008;40(4):407–26. Available from: http://dx.doi.org/10.108 0/15391523. 20 08.10782514
 Bernard RM, Abrami PC, Borokhovski E, Wade CA, Tamim RM, Surkes MA, et al. A meta-analysis of three types of interaction treatments in distance education. Rev Educ Res [Internet]. 2009;79(3):1243–89. Available from: ht t p:// dx.doi.org/10.3102/0034654309333844
Alqurashi  E.  Predicting  student satisfaction and perceived learning within online learning environments. Distance Educ  [Internet].  2019;40(1):133–48. Available from: http://dx.doi.org/10.108 0/01587919.2018.1553562
Ngo J, Institut Sains dan Teknologi Terpadu Surabaya, Budiyono Y, Ngadiman A, Universitas Katolik Widya Mandala Surabaya, Universitas Katolik Widya Mandala Surabaya. Investigating student satisfaction in remote online learning settings during covid-19 in indonesia. J Int Comp Educ [Internet]. 2021;10(2):73–95. Available from: htt p://dx.doi.org/10.14425/ jice.2021.10.2.0704
Chang KY. Factors affecting student satisfaction in different learning deliveries. 2013. Dissertation thesis]. [Illinois State (EE.UU.)]: Illinois State University.
Naziri  G,  Syforiyan  H,  Bahari  F. Investigate the relationship between course satisfaction and general health Martyr Beheshti University of Medical Sciences. Journal of Medicine and purification of Health Education Medical. 2012;
Mohtaram M, Torkzadeh J. A Study of the Relationship between Type of Organizational Structure of University and Departmental Social Capital with St udents’ Academ ic Sat isfact ion at Sh i ra z University. Journal of Applied Sociology. 2014;25(1):175 – 94.
Sevari K. The causal relationship of educational interactions with academic satisfaction mediated by academic self-regulatory learning. Psychological Achievements [Internet]. 2015;22(2):171– 88.  Available  from:  ht t p://d x.doi. org/10.22055/psy.2016.12315
Noughani F, Bayat Rizi M, Ghorbani Z, Ramim T. Correlation between emotional intelligence and educational consent of 
students of Tehran University of Medical Students. Tehran Univ Med J 2015; 73 (2) :110-116 URL: http://tumj.tums.ac.ir/ article-1-6607-fa.html
Broder HL, McGrath C, Cisneros GJ. Questionnaire development: face validity and item impact testing of the Child Oral Health Impact Profile. Community Dental and Oral Epidemiology. 2007; 35:8-19.
Lawshe CH. A quantitative approach to content validity. Personnel psychology. 19 75; 2 8 (4):5 63 –75.  d o i :  10 .1111/ j.174 4 - 6570.1975.tb01393.x
Waltz C. F., Bausell B. R. Nursing research: design statistics and computer analysis. Davis FA. 1981.
Matsunaga M. How to factor-analyze your data right: Do’s, don’ts, and how- to’s. Int J Psychol Res. 2010;3. doi: 10.2150 0/20112084.854.
Torrado M, Blanca MJ. Assessing satisfaction with online courses: Spanish version of the Learner Satisfaction Su r vey. Front Psychol [I nter net]. 2022;13. Available from: http://dx.doi.org/10.3389/ fpsyg.2022.875929
Devaux M, Sassi F. Social disparities in hazardous alcohol use: Self-report bias may lead to incorrect estimates. Eur J Public Health. 2016;26(1):129-134. doi:10.1093/eu r pub/ck v190

Files

Share

How to cite

Statistics