Exploring critical factors of the perceived usefulness of blended learning for higher education students

Autoři: Damijana Keržič aff001;  Nina Tomaževič aff001;  Aleksander Aristovnik aff001;  Lan Umek aff001
Působiště autorů: Faculty of Public Administration, University of Ljubljana, Ljubljana, Slovenia aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223767


Performed in a Slovenian higher education institution, the presented research was designed to help investigate which factors influence the ways a student perceives an e-course’s usefulness in a blended learning environment. The study is based on an online questionnaire completed by 539 students whose participation in the survey was voluntary. Using structural equation modelling, the students’ perceptions of different aspects were investigated, including their attitudes to course topics and technology, learning preferences, teachers’ role in course design and managing the teaching process. The empirical results show e-learning is positively perceived to be usefulness when: (1) the teacher is engaged and their activities in an e-course, with the (2) a student’s attitude to the subject matter and the lecturer’s classroom performance having a direct impact, and (3) technology acceptance having an indirect impact. No major differences were revealed when the model was tested on student subgroups sorted by gender, year of study, and students’ weekly spare-time activities.

Klíčová slova:

Computers – Human learning – Learning – Lectures – Questionnaires – Schools – Surveys – Teachers


1. Moore JL, Dickson-Deane C, Galyen K. e-Learning, online learning, and distance learning environments: Are they the same? Internet and High Edu, 2011; 14 (2011): 129–135. doi: 10.1016/j.iheduc.2010.10.001

2. Aparicio M, Bacao F, Oliveira T. Grit in the path to e-learning success. Comput Human Behav. 2017; 66: 388–399. doi: 10.1016/j.chb.2016.10.009

3. Bostrom C. Educational leadership and the e-learning paradigm. Global Partners in Education Journal. 2012; 2(1): 42–56. Available from: http://www.gpejournal.org/index.php/GPEJ/article/view/39/pdf

4. Islam AKMN. Investigating e-learning system usage outcomes in the university context. Comput Educ. 2013; 69: 387–399. doi: 10.1016/j.compedu.2013.07.037

5. Wu J-H, Tennyson RD, Hsia T-L. A study of student satisfaction in a blended e-learning system environment. Comput Educ. 2010; 55: 155–164. doi: 10.1016/j.compedu.2009.12.012

6. Owston R, York DN. The nagging question when designing blended courses: Does the proportion of time devoted to online activities matter? Internet and High Edu, 2018; 36: 22–32. doi: 10.1016/j.iheduc.2017.09.001

7. Graham CR, Woodfield W, Harrison JB. A framework for institutional adoption and implementation of blended learning in higher education. Internet High Educ. 2013; 18: 4–14. doi: 10.1016/j.iheduc.2012.09.003

8. Sun J. Multi-dimensional alignment between online instruction and course technology: A learner-centered perspective. Comput Educ. 2016; 101: 102–114. doi: 10.1016/j.compedu.2016.06.003

9. Walace L, Young J. Implementing blended learning: Policy implications for universities. Online Journal of Distance Learning Administration. 2010; 13(4): 7.

10. Diep A-N, Zhu C, Struyven K, Blieck Y. Who or what contributes to student satisfaction in different blended learning modalities? Br J Educ Technol. 2017; 48(2): 473–489. doi: 10.1111/bjet.12431

11. Willging PA, Johnson SD. Factors that influence students’ decision to drop out of online courses. Journal of Asynchronous Learning Network. 2004; 8(4): 105–118.

12. Ifinedo P, Pyke J, Anwar A. Business undergraduates’ perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics. 2018; 35: 93–102. doi: 10.1016/j.tele.2017.10.001

13. Roach V, Lemasters L. Satisfaction with online learning: A comparative descriptive study. Journal of Interactive Online Learning. 2006; 5(6): 317–332. Available from: www.ncolr.org/jiol/issues/pdf/5.3.7.pdf

14. Wu J, Liu W. An Empirical Investigation of the Critical Factors Affecting Students’ Satisfaction in EFL Blended Learning. Journal of Language Teaching and Research. 2013; 4(1): 176–185. doi: 10.4304/jltr.4.1.176-185

15. Alsabawy AY, Cater-Steel A, Soar J. Determinants of perceived usefulness of e-learning systems. Comput Human Behav. 2016; 64: 843–858. doi: 10.1016/j.chb.2016.07.065

16. Upadhyaya KT, Mallik D. E-learning as a socio-technical system: An insight into factors influencing its effectiveness. Business Perspectives and Research. 2013; 2(1): 1–12. doi: 10.1177/2278533720130101

17. Milic NM, Trajkovic GZ, Bukumiric ZM, Cirkovic A, Nikolic IM, Milin JS, et al. Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum. PLoS ONE. 2016; 11(2): e0148882. doi: 10.1371/journal.pone.0148882 26859832

18. Sun P-C, Tsai RJ, Finger G, Chen Y-Y, Yeh D. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput Educal. 2008; 50: 1183–1202. doi: 10.1016/j.compedu.2006.11.007

19. Lin W-S, Wang C-H. Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Comput Educ. 2012; 58: 88–99. Available from: https://www.learntechlib.org/p/50709/

20. Beyth-Marom R, Chajut E, Rocas S, Sagiv L. Internet assisted versus traditional distance learning environments: factors affecting students’ preferences. Comput Educ. 2003; 41(1): 65–76. doi: 10.1016/S0360-1315(03)00026-5

21. Sun J. Multi-dimensional alignment between online instruction and course technology: A learner-centered perspective. Comput Educ. 2016; 101: 102–114. doi: 10.1016/j.compedu.2016.06.003

22. Eom SB, Wen HJ. The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education. 2006; 4(2): 215–235. doi: 10.1111/j.1540-4609.2006.00114.x

23. Webster J, Hackley P. Teaching effectiveness in technology-mediated distance learning. Acad Manage J. 1997; 40(6): 1282–1309. doi: 10.5465/257034

24. Almerich G, Orellana N, Suarez-Rodriguez J, Diaz-Garcia I. Teachers’ information and communication technology competences: A structural approach. Comput Educ. 2016; 100: 110–125. doi: 10.1016/j.compedu.2016.05.002

25. Volery T, Lord D. Critical success factors in online education. International Journal of Educational Management. 2000; 14(5): 216–223. doi: 10.1108/09513540010344731

26. Garrison DR. E-learning in the 21st century: A framework for research and practice. 2nd ed. New York, NY: Routledge; 2011.

27. Park SY. An analysis of the technology acceptance model in understanding university students’ behavioural intention to use e-learning. J Educ Techno Soc. 2009; 12(3): 150–162. Available from: http://www.jstor.org/stable/jeductechsoci.12.3.150

28. Boelens R, De Wever B, Voet M. Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review. 2017; 22: 1–18. doi: 10.1016/j.edurev.2017.06.001

29. Yeou M. An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 2016; 44(3): 300–318. doi: 10.1177/0047239515618464

30. Vo HM, Zhu C, Diep NA. The effect of blended learning on student performance at course-level in higher education: A meta-analysis. Studies in Educational Evaluation. 2017; 53: 17–28. doi: 10.1016/j.stueduc.2017.01.002

31. Umek L, Keržič D, Tomaževič N, Aristovnik A. Analysis of selected aspects of students`performance and satisfaction in a Moodle-based e-learning system environment. Eurasia Journal of Mathematics, Science and Technology Education. 2015; 11(6): 1495–1505. doi: 10.12973/eurasia.2015.1408a

32. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989; 13(3): 319–340. doi: 10.2307/249008

33. Abdulah F, Ward R, Ahmed E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput Human Behav. 2016; 63: 75–90. doi: 10.1016/j.chb.2016.05.014

34. Ramirez-Correa PE, Arenas-Gaitán J, Rondán-Cataluna FJ. Gender and acceptance of e-learning: a multi group analysis based on structural equation model among college students in Chile and Spain. PLoS ONE. 2015; 10(10): 1–17. doi: 10.1371/journal.pone.0140460 26465895

35. Davis FD, Bogozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manage Sci. 1989; 35(8): 982–1003. doi: 10.1287/mnsc.35.8.982

36. Joo YJ, Lim KY, Kim EK. Online university students’ satisfaction and persistence: Examining perceived level of presence, usefulness and ease of use as predictor in a structural model. Comput Educ. 2011; 57(2): 1654–1664. Available from: https://www.learntechlib.org/p/50744/

37. Hsieh JJPA, Cho WSV. Comparing e-Learning tools’ success: The case of instructor-student interactive vs. self-paced tools. Comput Educ. 2011; 57(3): 2025–2038. doi: 10.1016/j.compedu.2011.05.002

38. Liaw S-S. Investigating students’ perceived satisfaction, behavioural intention, and effectiveness of e-learning: A case study of the Blackboard system. Comput Educ. 2008; 51: 864–873. doi: 10.1016/j.compedu.2007.09.005

39. Chang C-T, Hajiyev J, Su C-R. Examining the students’ behavioural intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Comput Educ. 2017; 111: 128–143.

40. Tarhini A, Hone K, Liu X. A cross-cultural examination of the impact of social, organizational and individual factors on educational technology acceptance between British and Lebanese university students. Br J Educ Technol. 2015; 46(4): 739–755. doi: 10.1111/bjet.12169

41. Manwaring KC, Larsen R, Graham CR, Henrie CR. Investigating student engagement in blended learning settings using experience sampling and structural equation modelling. Internet High Educ. 2017; 35: 21–33. doi: 10.1016/j.iheduc.2017.06.002

42. Feldman KA. Identifying exemplary teachers and teaching: Evidence from student ratings. In Perry RP, Smart JC, editors. The scholarship of teaching and learning in higher education: An evidence-based perspective. Dordrecht, The Netherlands: Springer; 2007. p. 93–143. https://www.wku.edu/senate/archives/archives_2015/e-4-k-feldman-identifying-exemplary-teachers-and-teaching-evidence-from-student-ratings-research.pdf

43. Alhija FN. Teaching in higher education: Good teaching through students’ lens. Studies in Educational Evaluation. 2017; 54: 4–12. doi: 10.1016/j.stueduc.2016.10.006

44. Casero A. Modulating factors in the perception of teaching quality. RELIEVE. 2010; 16(2): art. 3. http://www.uv.es/RELIEVE/v16n2/RELIEVEv16n2_3eng.htm

45. Ellis RA, Ginns P, Piggott L. E-learning in higher education: some aspects and their relationship to approaches to study. Higher Education Research & Development. 2009; 28(3): 303–318. doi: 10.1080/07294360902839909

46. Kaiser HF. The application of electronic computers to factor analysis. Educational and Psychological Measurements. 1960; 20: 141–151. doi: 10.1177/001316446002000116

47. Cliff N. Eigenvalues-Greater-Than-One-Rule and the Reliability of Components. American Psychological Association, Inc. University of Southern California. California: Psychological Bulletin. 1988.

48. MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modelling. 1996; 1: 130–149. doi: 10.1037/1082-989X.1.2.130

49. Robinson JP, Shaver PR, Wrightsman LS. Criteria for scale selection and evaluation. In Robinson JP, et al. editors. Measures of Personality and Social Psychological Attitudes. San Diego, California: Academic Press; 1991. p. 1–16.

50. Gefen D, Straub D. A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the Association for Information Systems. 2005; 16(1): 91–109. Available from: https://pdfs.semanticscholar.org/a287/0e379cbff593811b8b918ba6323c12ac7d83.pdf

51. Hair JF, Tatham RL, Anderson RE, Black W. Multivariate data analysis. 5th ed. Prentice-Hall: London; 1998.

52. Aristovnik A, Keržič D, Tomaževič N, Umek L. Demographic determinants of usefulness of e-learning tools among students of public administration. Interactive Technology and Smart Education. 2016; 13(4): 289–304. doi: 10.1108/ITSE-09-2016-0033

53. Severiens SE, Ten Dam GTM. Gender differences in learning styles: A narrative review and quantitative meta-analysis. High Educ. 1994; 27(4): 487–501. doi: 10.1007/BF01384906

54. Wehrwein EA, Lujan HL, DiCarlo SE. Gender differences in learning style preferences among undergraduate physiology students. Adv Physiol Educ. 2007; 31(2): 153–157. doi: 10.1152/advan.00060.2006 17562903

55. Cai Z, Fan X, Du J. Gender and attitudes toward technology use: A meta-analysis. Comput Educ. 2017; 105: 1–13. doi: 10.1016/j.compedu.2016.11.003

56. Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, et al. Orange: Data Mining Toolbox in Python. J Mach Learn Res. 2013 Aug 14: 2349–2353. Available from: https://pdfs.semanticscholar.org/bb14/4c04b9eb44579b19d21c3d5954401408440b.pdf

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2019 Číslo 11