Factorial validation of the university students’ attitudes toward blended learning scale: An exploratory and confirmatory analysis
Taha O. Alkursheh 1 *
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1 University of Tabuk, Saudi Arabia
* Corresponding Author


The importance of understanding student attitudes has become paramount in the successful deployment of blended learning. This study aimed to examine the factorial structure of a scale designed to assess university students' attitudes towards blended learning. Using a descriptive quantitative research approach, the study included a sample of 889 male and female students from the University of Tabuk, located in the Kingdom of Saudi Arabia. The participants were selected randomly from different academic majors and levels of study. The instrument employed in this study was the Blended Learning Attitudes Scale, a tool designed by the researcher and subjected to rigorous validation procedures. The researcher utilised exploratory and confirmatory factor analyses to understand the latent variables the scale represented comprehensively. The study's findings indicated the presence of a three-factor model, encompassing participants' perceptions of the nature of blended learning, its perceived importance, and their willingness to utilise it. The combined influence of these three factors accounted for 64% of the observed variance. The scale had noteworthy psychometric features, as evidenced by its high-reliability coefficients and robust validity indicators. This study presents a reliable instrument that educators and researchers may utilise to assess university students' attitudes towards blended learning.  



  • Acal, C., Aguilera, A. M., & Escabias, M. (2020). New modeling approaches based on varimax rotation of functional principal components. Mathematics, 8(11), 2085. https://doi.org/10.3390/math8112085
  • Akkoyunlu, B., & Yılmaz-Soylu, M. (2008). Development of a scale on learners’ views on blended learning and its implementation process. The Internet and Higher Education, 11(1), 26-32. https://doi.org/10.1016/j.iheduc.2007.12.006
  • Al-khresheh, M. (2022). The impact of COVID-19 pandemic on teachers’ creativity of online teaching classrooms in the Saudi EFL context. Frontiers in Education, 7(1041446), 1-11. https://doi.org/10.3389/feduc.2022.1041446
  • Al-khresheh, M. (2023). Virtual classrooms engagement among Jordanian EFL students during the pandemic of COVID-19 period. Cogent Education, 10(1), 1-22.https://doi.org/10.1080/2331186X.2023.2188989
  • AL-Qadri, A. H., Zhao, W., Li, M., Al-khresheh, M. H., & Boudouaia, A. (2021). The prevalence of the academic learning difficulties: An observation tool. Heliyon, 7(10), e08164. https://doi.org/10.1016/j.heliyon.2021.e08164
  • Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science, 7(4), 2029–2040. https://doi.org/10.5267/j.ijdns.2023.6.010
  • Bedebayeva, M., Grinshkun, V., Kadirbayeva, R., Zhamalova, K., & Suleimenova, L. (2022). A blended learning approach for teaching computer science in high schools. Cypriot Journal of Educational Sciences, 17(7), 2235–2246. https://doi.org/10.18844/cjes.v17i7.7693
  • Bhagat, K. K., Cheng, C. H., Koneru, I., Fook, F. S., & Chang, C. Y. (2021). Students’ blended learning course experience scale (BLCES): Development and validation. Interactive Learning Environments, 31(6), 3971–3981. https://doi.org/10.1080/10494820.2021.1946566
  • Bouilheres, F., Le, L. T. V. H., McDonald, S., Nkhoma, C., & Jandug-Montera, L. (2020). Defining student learning experience through blended learning. Education and Information Technologies, 25(4), 3049–3069. https://doi.org/10.1007/s10639-020-10100-y
  • Bruggeman, B., Tondeur, J., Struyven, K., Pynoo, B., Garone, A., & Vanslambrouck, S. (2021). Experts speaking: Crucial teacher attributes for implementing blended learning in higher education. The Internet and Higher Education, 48, 100772. https://doi.org/10.1016/j.iheduc.2020.100772
  • Byrne, B. (2010). Structural equation modelling with Amos, basic concepts, applications, and programming. Routledge Taylor & Francis Group.
  • Cheung, S. K. S., & Wang, F. L. (2019). Blended learning in practice: Guest editorial. Journal of Computing in Higher Education, 31(2), 229–232. https://doi.org/10.1007/s12528-019-09229-8
  • Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
  • Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15, 1-16. https://doi.org/10.1186/s41239-017-00875
  • Ginns, P., & Ellis, R. A. (2009). Evaluating the quality of e‐learning at the degree level in the student experience of blended learning. British Journal of Educational Technology, 40(4), 652-663. https://doi.org/10.1111/j.1467-8535.2008.00861.x
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010) Multivariate data analysis (7th Edition). Pearson.
  • Halverson, L. R., & Graham, C. R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23(2) 145-178. https://doi.org/10.24059/olj.v23i2.1481
  • Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015, December). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005
  • Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
  • Kumar, A., Krishnamurthi, R., Bhatia, S., Kaushik, K., Ahuja, N. J., Nayyar, A., & Masud, M. (2021). Blended learning tools and practices: A comprehensive analysis. IEEE Access, 9, 85151–85197. https://doi.org/10.1109/access.2021.3085844
  • Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. PLOS ONE, 15(7), e0235957. https://doi.org/10.1371/journal.pone.0235957
  • Lecerf, T., & Canivez, G. L. (2018). Complementary exploratory and confirmatory factor analyses of the French WISC–V: Analyses based on the standardization sample. Psychological Assessment, 30(6), 793–808. https://doi.org/10.1037/pas0000526
  • Lim, C. P., & Graham, C. R. (Eds.). (2021). Blended learning for inclusive and quality higher education in Asia. Springer.
  • Liu, X. (2021). Primary science curriculum student acceptance of blended learning: structural equation modeling and visual analytics. Journal of Computers in Education, 9(3), 351–377. https://doi.org/10.1007/s40692-021-00206-8
  • López-Pérez, M. V., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students’ perceptions and their relation to outcomes. Computers & Education, 56(3), 818–826. https://doi.org/10.1016/j.compedu.2010.10.023
  • Luo, L., Pibulcharoensit, S., Kitcharoen, K., & Feng, D. (2022). Exploring behavioural intention towards hybrid education of undergraduate students in public universities in Chongqing, China. AU-GSB e-JOURNAL, 15(2), 178-186. https://doi.org/10.14456/augsbejr.2022.83
  • Mali, D., & Lim, H. (2021). How do students perceive face-to-face/blended learning as a result of the Covid-19 pandemic? The International Journal of Management Education, 19(3), 2-17. https://doi.org/10.1016/j.ijme.2021.100552
  • Marinagi, C., & Skourlas, C. (2013). Blended learning in personalized assistive learning environments. International Journal of Mobile and Blended Learning, 5(2), 39–59. https://doi.org/10.4018/jmbl.2013040103
  • Masitoh, F., & Sufirmansyah, S. (2022). Google classroom application in blended learning: Indonesian EFL learners’ perception. Education and Linguistics Knowledge Journal, 4(1), 1. https://doi.org/10.32503/edulink.v4i1.2378
  • Matosas-López, L., Aguado-Franco, J., & Gómez-Galán, J. (2019). Constructing an instrument with behavioral scales to assess teaching quality in blended learning modalities. Journal of New Approaches in Educational Research, 8(2), 142-165. https://doi.org/10.7821/naer.2019.7.410
  • McGee, P., & Reis, A. (2012). Blended course design: a synthesis of best practices. Online Learning, 16(4), 7-22. https://doi.org/10.24059/olj.v16i4.239
  • Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. US Department of Education.
  • Megahed, N., & Hassan, A. (2021). A blended learning strategy: reimagining the post-Covid-19 architectural education. International Journal of Architectural Research, 16(1), 184–202. https://doi.org/10.1108/arch-04-2021-0081
  • Morin, A. J., Meyer, J. P., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231-254. https://doi.org/10.1177/10944281156211
  • Mudjijanti, F., & Srimulyani, V. A. (2023,). The impact of e-learning on student interest during the Covid-19 pandemic. European Journal of Education and Pedagogy, 4(3), 135–142. https://doi.org/10.24018/ejedu.2023.4.3.693
  • Padilla Rodriguez, B. & Armellini, A. (2017). Developing self-efficacy through a massive open online course on study skills. Open Praxis, 9(3), 335-343.
  • Petronzi, R., & Petronzi, D. (2020). The online and campus (OaC) model as a sustainable blended approach to teaching and learning in higher education: A response to COVID-19. Journal of Pedagogical Research, 4(4), 498-507. https://doi.org/10.33902/JPR.2020064475
  • Poon, J. (2013) Blended learning: An institutional approach for enhancing students’ learning experiences. Journal of Online Learning and Teaching, 9, 271-288.
  • Rivera, J. (2019) Blended learning-effectiveness and application in teaching and learning foreign languages. Open Journal of Modern Linguistics, 9, 129-144. https://doi.org/10.4236/ojml.2019.92013
  • Rodríguez-Mantilla, J. M., Fernández-Díaz, M. J., & Carrascosa, V. L. (2019). Validation of a questionnaire to evaluate the impact of ISO 9001 Standards in schools with a Confirmatory Factor Analysis. Studies in Educational Evaluation, 62, 37-48, https://doi.org/10.1016/j.stueduc.2019.03.013
  • Şentürk, C. (2021). Effects of the blended learning model on preservice teachers’ academic achievements and twenty-first century skills. Education and Information Technologies, 26(1), 35–48. https://doi.org/10.1007/s10639-020-10340-y
  • Sergi, M. R., Picconi, L., Saggino, A., Fermani, A., Bongelli, R., & Tommasi, M. (2023). Psychometric properties of a new instrument for the measurement of the perceived quality of distance learning during the coronavirus disease 2019 (COVID-19) pandemic. Frontiers in Psychology, 14, 1169957. https://doi.org/10.3389/fpsyg.2023.1169957
  • Shakeel, S. I., Haolader, M. F. A., & Sultana, M. S. (2023). Exploring dimensions of blended learning readiness: Validation of scale and assessing blended learning readiness in the context of TVET Bangladesh. Heliyon, 9(1), e12766. https://doi.org/10.1016/j.heliyon.2022.e12766
  • Suhr, D. (2006). The Basics of structural equation modeling. university of northern colorado. statistic and data analysis. Retrieved June 20, 2023 from http://www.lexjansen.com/wuss/2006/tutorials/TUT-Suhr.pdf
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Tartakovsky, E. (2016). Personal value preferences and burnout of social workers. Journal of Social Work, 16(6), 657-673. https://doi.org/10.1177/1468017315589872
  • 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. Learning Environments Research, 8(3), 289–308. https://doi.org/10.1007/s10984-005-1568-3
  • Warne, R., & Larsen, R. (2014). Evaluating a proposed modification of the Guttman rule for determining the number of factors in an exploratory factor analysis. Psychological Test and Assessment Modeling, 56(1),104-123.
  • Xiang, C., & Duangekanong, S. (2022). Factors affecting student satisfaction with blended instruction for the “digital image fundamental” course at Chengdu University. International Journal of Information and Education Technology, 12(3), 232–238. https://doi.org/10.18178/ijiet.2022.12.3.1609
  • Yusuf, M., Andariana, A., Bte Abustang, P., Mannan, A., Tabbu, M. A. S., Qaiyimah, D., & Haris. (2023). Construction Validity Testing on Blended Learning Implementation Evaluation Instruments. E3S Web of Conferences, 400, 01007. https://doi.org/10.1051/e3sconf/202340001007
  • Zheng, W., Yu, F., & Wu, Y. J. (2021). Social media on blended learning: The effect of rapport and motivation. Behaviour & Information Technology, 41(9), 1941–1951. https://doi.org/10.1080/0144929x.2021.1909140


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