Volume 18, Issue 3 (2025)                   JMED 2025, 18(3): 90-98 | Back to browse issues page

Ethics code: IR.ZAUMS.REC.1402.235


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Arab Borzu Z, Hegazi I, Amiri Moghadam M H, Bakhtiyari M, Hosseini Koukamari P. Translation and psychometric evaluation of a self-regulated learning questionnaire for blended learning among Iranian students. JMED 2025; 18 (3) :90-98
URL: http://edujournal.zums.ac.ir/article-1-2315-en.html
1- Department of Epidemiology & Biostatistics, Zahedan University of Medical Sciences, Zahedan, Iran
2- Medical Education Unit, School of Medicine, Western Sydney University, Campbelltown, Australia
3- Student Research Committee, Zahedan University of Medical Sciences, Zahedan, Iran
4- Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
Abstract:   (1000 Views)
Background & Objective: The rapid advancement of the Internet and technology has enabled the widespread adoption of blended learning in medical education. However, there is no validated Persian scale to measure self-regulated learning in blended learning among Iranian students. This study aims to fill this gap by translating and validating an existing tool for assessing self-regulated learning in a blended learning environment among Iranian students.
Materials & Methods: The forward-backward method was used to translate the original English questionnaire into Persian. After assessing face and content validity, the Persian version was evaluated for its psychometric properties among 330 students from Zahedan Medical University in Iran. Construct validity was analyzed using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). To ensure reliability, we calculated the Average Inter-Item Correlation (AIC), Cronbach's alpha, and McDonald's omega. Additionally, convergent and discriminant validity were examined using Average Variance Extracted (AVE), Maximum Shared Variance (MSV), and Fornell and Larcker's criteria.
Results: The findings revealed that the Persian version of the Blended Learning Questionnaire (BLQ) consists of four distinct factors: Accessibility and Guidance (4 items), Social and Contextual (4 items), Delivery of Content (6 items), and Intrinsic and Extrinsic (2 items). Together, these factors accounted for 52.43% of the total variance in the BLQ. The results from the CFA indicated that all goodness-of-fit metrics supported the adequacy of the model. Additionally, the Cronbach's alpha, McDonald's omega, and Composite Reliability (CR) scores were all greater than 0.7, demonstrating strong internal consistency. Moreover, the indices showed acceptable levels of both convergent and discriminant validity for the Persian version of the BLQ.
Conclusion: The study's findings indicated that the Persian version of the BLQ demonstrated acceptable validity and reliability among Iranian students, making it suitable for academic and research purposes in Persian-speaking countries.



 
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Article Type : Orginal Research | Subject: Medical Education
Received: 2024/10/26 | Accepted: 2025/07/14 | Published: 2025/10/1

References
1. Allen IE, Seaman J. Changing course: ten years of tracking online education in the United States [Internet]. Sloan Consortium; 2013. Available from: [cited 2025 Jun 10].
2. Van Houten‐Schat MA, Berkhout JJ, Van Dijk N, Endedijk MD, Jaarsma AD, Diemers AD. Self‐regulated learning in the clinical context: a systematic review. Med Educ. 2018;52(10):1008-15. [DOI:10.1111/medu.13615] [PMID] []
3. Porter WW, Graham CR, Spring KA, Welch KR. Blended learning in higher education: institutional adoption and implementation. Comput Educ. 2014;75:185-95. [DOI:10.1016/j.compedu.2014.02.011]
4. Graham CR. Emerging practice and research in blended learning. In: Moore JL, Dickson-Deane C, Galyen K, editors. Handbook of distance education. 3rd ed. New York: Routledge; 2013. p. 333-50.
5. Garrison DR, Vaughan ND. Blended learning in higher education: framework, principles, and guidelines. Hoboken: John Wiley & Sons; 2008. [DOI:10.1002/9781118269558]
6. 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. 2009;79(3):1243-89. [DOI:10.3102/0034654309333844]
7. Zimmerman BJ. Becoming a self-regulated learner: an overview. Theory Pract. 2002;41(2):64-70. [DOI:10.1207/s15430421tip4102_2]
8. Schunk DH, Zimmerman BJ. Motivation and self-regulated learning: theory, research, and applications. New York: Routledge; 2012. [DOI:10.4324/9780203831076] []
9. Pintrich PR. A conceptual framework for assessing motivation and self-regulated learning in college students. Educ Psychol Rev. 2004;16:385-407. [DOI:10.1007/s10648-004-0006-x]
10. Zhang D, Zhao JL, Zhou L, Nunamaker JF. Can e-learning replace classroom learning? Commun ACM. 2004;47(5):75-9. [DOI:10.1145/986213.986216]
11. Artino Jr AR, Stephens JM. Academic motivation and self-regulation: a comparative analysis of undergraduate and graduate students learning online. Internet High Educ. 2009;12(3-4):146-51. [DOI:10.1016/j.iheduc.2009.02.001]
12. Broadbent J, Poon WL. Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. Internet High Educ. 2015;27:1-13. [DOI:10.1016/j.iheduc.2015.04.007]
13. Dent AL, Koenka AC. The relation between self-regulated learning and academic achievement across childhood and adolescence: a meta-analysis. Educ Psychol Rev. 2016;28:425-74. [DOI:10.1007/s10648-015-9320-8]
14. Pintrich PR, Smith DA, Garcia T, McKeachie WJ. Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educ Psychol Meas. 1993;53(3):801-13. [DOI:10.1177/0013164493053003024]
15. Ballouk R, Mansour V, Dalziel B, Hegazi I. The development and validation of a questionnaire to explore medical students' learning in a blended learning environment. BMC Med Educ. 2022;22:1-9. [DOI:10.1186/s12909-021-03045-4] [PMID] []
16. Cattell R. The scientific use of factor analysis in behavioral and life sciences. New York: Springer Science & Business Media; 2012.
17. Nunnally JC. An overview of psychological measurement. In: Wetzler S, Katz MM, editors. Clinical diagnosis of mental disorders: a handbook. New York: Springer; 1978. p. 97-146. [DOI:10.1007/978-1-4684-2490-4_4]
18. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine. 2000;25(24):3186-91. [DOI:10.1097/00007632-200012150-00014] [PMID]
19. Costello AB, Osborne J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval. 2005;10(1).
20. Field A. Discovering statistics using SPSS: introducing statistical method. 3rd ed. Thousand Oaks: Sage Publications; 2009.
21. Winne PH. A perspective on state-of-the-art research on self-regulated learning. Instr Sci. 2005;33(5-6):559-65. [DOI:10.1007/s11251-005-1280-9]
22. Society of Multivariate Experimental Psychology. Multivariate behavioral research monographs [Internet]. Fort Worth: Society of Multivariate Experimental Psychology; 1967. Available from:[cited 2025 Jun 10].
23. Stevens JP. Exploratory and confirmatory factor analysis. In: Stevens JP, editor. Applied multivariate statistics for the social sciences. 5th ed. New York: Routledge; 2012. p. 337-406. [DOI:10.4324/9780203843130-15]
24. Tavakol M, Dennick R. Making sense of Cronbach's alpha. Int J Med Educ. 2011;2:53. [DOI:10.5116/ijme.4dfb.8dfd] [PMID] []
25. Vinzi VE, Chin WW, Henseler J, Wang H. Perspectives on partial least squares. In: Vinzi VE, Chin WW, Henseler J, Wang H, editors. Handbook of partial least squares: concepts, methods and applications. Berlin: Springer; 2009. p. 1-20. [DOI:10.1007/978-3-540-32827-8_1]
26. Borzu ZA, Karimy M, Leitão M, Pimenta F, Albergaria R, Khoshnazar Z, et al. Validation of the menopause representation questionnaire (MenoSentations-Q) among Iranian women and cross-cultural comparison with Portuguese women. BMC Womens Health. 2025;25(1):87. [DOI:10.1186/s12905-025-03606-5] [PMID] []
27. Clark LA, Watson D. Constructing validity: basic issues in objective scale development. In: Zumbo BD, Chan EK, editors. Validity and validation in social, behavioral, and health sciences. Cham: Springer; 2014. p. 187-203. [DOI:10.1037/14805-012] []
28. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39-50. https://doi.org/10.1177/002224378101800104 [DOI:10.2307/3151312]
29. Nikolopoulou K, Zacharis G. Blended learning in a higher education context: exploring university students' learning behavior. Educ Sci. 2023;13(5):514. [DOI:10.3390/educsci13050514]
30. Duncan T, Pintrich P, Smith D, McKeachie W. Motivated strategies for learning questionnaire (MSLQ) manual. Ann Arbor: University of Michigan; 2015.
31. Van der Westhuizen CP, Maphalala MC, Bailey R, editors. Blended learning environments to foster self-directed learning. Cape Town: AOSIS; 2022.
32. Dahmash NB. I couldn't join the session': benefits and challenges of blended learning amid covid-19 from EFL students. Int J Engl Linguist. 2020;10(5):221-30. [DOI:10.5539/ijel.v10n5p221]
33. Derntl M, Motschnig-Pitrik R. The role of structure, patterns, and people in blended learning. Internet High Educ. 2005;8(2):111-30. [DOI:10.1016/j.iheduc.2005.03.002]
34. De Brito Lima F, Lautert SL, Gomes AS. Learner behaviors associated with uses of resources and learning pathways in blended learning scenarios. Comput Educ. 2022;191:104625. [DOI:10.1016/j.compedu.2022.104625]
35. Wong R. Basic psychological needs of students in blended learning. Interact Learn Environ. 2022;30(6):984-98. [DOI:10.1080/10494820.2019.1703010]
36. Chiu TK. Applying the self-determination theory (SDT) to explain student engagement in online learning during the covid-19 pandemic. J Res Technol Educ. 2022;54(sup1):S14-S30. [DOI:10.1080/15391523.2021.1891998]
37. Green RA, Whitburn LY, Zacharias A, Byrne G, Hughes DL. The relationship between student engagement with online content and achievement in a blended learning anatomy course. Anat Sci Educ. 2018;11(5):471-7. [DOI:10.1002/ase.1761] [PMID]
38. Foerst NM, Klug J, Jöstl G, Spiel C, Schober B. Knowledge vs. action: discrepancies in university students' knowledge about and self-reported use of self-regulated learning strategies. Front Psychol. 2017;8:1288. [DOI:10.3389/fpsyg.2017.01288] [PMID] []
39. Papinczak T. Are deep strategic learners better suited to PBL? A preliminary study. Adv Health Sci Educ. 2009;14:337-53. [DOI:10.1007/s10459-008-9115-5] [PMID]
40. Hosseini Ravesh R, Rezaiee R, Mosalanejad L. Validation of the Persian version of the short self-regulated learning questionnaire for medical students: a descriptive study. J Med Educ Dev. 2022;15(47):1-10. [DOI:10.52547/edcj.15.47.1]
41. Luo Y, Lin J, Yang Y. Students' motivation and continued intention with online self-regulated learning: a self-determination theory perspective. Z Erziehwiss. 2021;24(6):1379-99. [DOI:10.1007/s11618-021-01042-3] [PMID] []

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