Sage Journals: Discover world-class research

Abstract

This study investigated the influence of students’ cognitive instrumental processes (i.e., relevance for learning, the quality of learning outcomes, and result demonstrability) on students’ perceived usefulness and intention to use flipped learning. An extension of the technology acceptance model (TAM2) was employed in this study. A total of 277 undergraduate students enrolled in flipped classes at a Korean university completed an online survey for this study. The results of the cognitive instrumental processes revealed that the quality of learning outcomes influenced students’ perceived usefulness and intention to use flipped learning. This finding emphasizes that it is necessary to deliver high-quality classes using a flipped learning approach to satisfy students’ expectations of improving learning outcomes. The relevance for learning also affected students’ perceived usefulness of flipped learning classes and indirectly influenced their future intention to use flipped learning. The results emphasize that students should be given opportunities to recognize the relevance of flipped learning based on their expected learning achievement. Finally, result demonstrability did not influence perceived usefulness nor intention to use flipped learning. The research findings of this study have practical implications and recommendations for flipped class instructors to persuade students to enroll in flipped learning courses from the perspective of cognitive instrumental processes.

Keywords

flipped learning TAM2 cognitive instrumental process relevance for learning quality of learning outcomes

Get full access to this article

View all access options for this article.

References

Abdullah

Ward

(2016). Developing a general extended technology acceptance model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036

Agudo-Peregrina

A. F.

Hernandez-Garcia

Pascual-Miguel

F. J.

(2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314. https://doi.org/10.1016/j.chb.2013.10.035

Atkinson

J. W.

(1957). Motivational determinants of risk-taking behavior. Psychological Review, 64(6, Pt.1), 359–372.

Beach

L. R.

Mitchell

T. R.

(1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3(3), 439–449.

Bergmann

(2017). Solving the homework problem by flipping the learning. ASCD Publication.

Bergmann

Sams

(2012). Flip your classroom: Reach every student in every class every day. International Society for Technology in Education.

Betihavas

Bridgman

Kornhaber

Cross

(2016). The evidence for ‘flipping out’: A systematic review of the flipped classroom in nursing education. Nurse Education Today, 38, 15–21.

Bonk

C. J.

(2016). What is the state of e-learning? Reflections on 30 ways learning is changing. Journal of Open, Flexible and Distance Learning, 20(2), 6–20. http://www.jofdl.nz/index.php/JOFDL/article/view/300

Bonk

C. J.

Wiley

(2020). Preface: Reflections on the waves of emerging learning technology. Educational Technology Research and Development (Development), 68(4), 1595–1612. https://doi.org/10.1007/s11423-020-09809-x. https://link.springer.com/content/pdf/10.1007/s11423-020-09809-x.pdf

10.

Browne

M. W.

Cudeck

(1993). Alternative ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp.136–162). SAGE.

11.

Clark

R. E.

(1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. https://doi.org/10.3102/00346543053004445

12.

Clark

R. E.

(1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21–29.

13.

Davis

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

14.

Doo

M., Y.

Bonk

C. J.

(2020). The effects of self-efficacy, self-regulation and social presence on learning engagement in a large university class using flipped learning. Journal of Computer Assisted Learning, 36(6), 997–1010. https://doi.org/10.1111/jcal.12455

15.

Edwards

(1954). The theory of decision making. Psychological Bulletin, 51(4), 380–417. https://doi.org/10.1037/h0053870

16.

Elliott

E. S.

Dweck

C. S.

(1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54, 5–12.

17.

Fishbein

Ajzen

(1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison Wesley.

18.

Flip Learning. (2014, March 12). Definition of flipped learning. Flip Learning. https://flippedlearning.org/definition-of-flipped-learning

19.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

20.

Fulton

(2012, June). Upside down and inside out: Flip your classroom to improve student learning. Learning & Leading with Technology, 39(8), 12–17. https://files.eric.ed.gov/fulltext/EJ982840.pdf

21.

George

Mallery

(2010). SPSS for windows step by step: A simple guide and reference. Pearson Education.

22.

Hackman

J. R.

Oldham

G. R.

(1974). The job diagnostic survey: An instrument for the diagnosis of jobs and the evaluation of job redesign projects. Department of Administrative Sciences: Yale University.

23.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

Tatham

R. L.

(2006). Multivariant data analysis. Pearson International Edition.

24.

Hew

K. F.

Cheung

W. S.

(2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58. https://doi.org/10.1016/j.edurev.2014.05.001

25.

Hew

K. F.

C. K.

(2018). Flipped classroom improves student learning in health professions education: A meta-analysis. BMC Medical Education, 18(1), 38.

26.

Keller

J. M.

(1983). Motivational design of instruction. In Reigeluth

C. M.

(Ed.), Instructional-design theories and models: An overview of their current status (pp. 386–429). Lawrence Erlbaum.

27.

Koh

J. H. L.

(2019). Four pedagogical dimensions for understanding flipped classroom practices in higher education: A systematic review. Educational Sciences: Theory & Practice, 19(4), 14–33. https://doi.org/10.12738/estp.2019.4.002

28.

Kop

(2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. The International Review of Research in Open and Distributed Learning, 12(3), 19–38. https://doi.org/10.19173/irrodl.v12i3.882

29.

Kozma

R. B.

(1994). The influence of media on learning: The debate continues. School Library Media Quarterly, 22(4), 233–239.

30.

Locke

E. A.

Latham

G. P.

(1990). A theory of goal setting and task performance. Prentice-Hall.

31.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130

32.

Margaryan

Bianco

Littlejohn

(2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77–83. https://doi.org/10.1016/j.compedu.2014.08.005

33.

Martin

Dennen

V. P.

Bonk

C., J.

(2020). A synthesis of systematic review research on emerging learning environments and technologies. Educational Technology Research and Development (Development), 68(4), 1613–1634. https://doi.org/10.1007/s11423-020-09812-2. https://link.springer.com/content/pdf/10.1007/s11423-020-09812-2.pdf

34.

Mitchell

T. R.

Beach

L. R.

(1976). A review of occupational preference and choice research using expectancy theory and decision theory. Journal of Occupational Psychology, 49(4), 231–248.

35.

Moore

G. C.

Benbasat

(1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–191. http://dx.doi.org/10.1287/isre.2.3.192

36.

E. M. W.

(2018). Integrating self-regulation principles with flipped classroom pedagogy for first year university students. Computers & Education, 126, 65–74. https://doi.org/10.1016/j.compedu.2018.07.002

37.

O’Flaherty

Phillips

(2015). The use of flipped classrooms in higher education: A scoping review. The Internet and Higher Education, 25, 85–95. https://doi.org/10.1016/j.iheduc.2015.02.002

38.

Park

S. Y.

Nam

M. W.

Cha

S. B.

(2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x

39.

Parker, J. R., Becker, K., & Sawyer, B. (2008). Re-reconsidering research on learning from media: Comments on Richard E. Clark’s point of view column on serious games. Educational Technology Magazine, 2008, 39–43.

40.

Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

41.

Surry

D. W.

Land

S. M.

(2000). Strategies for motivating higher education faculty to use technology. Innovations in Education and Training International, 37(2), 145–153.

42.

Venkatesh

Bala

(2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

43.

Venkatesh

Davis

F. D.

(2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

44.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

45.

Vroom

V. H.

(1964). Work and motivation. Wiley.

46.

Wigfield

Eccles

(2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015

47.

Zainuddin

Halili

S. H.

(2016). Flipped classroom research and trends from different fields of study. The International Review of Research in Open and Distributed Learning, 17(3), 313–340. https://doi.org/10.19173/irrodl.v17i3.2274

Cognitive Instrumental Processes of Flipped Learners: Effects of Relevance for Learning,Quality of Learning Outcomes,and Result Demonstrability

Abstract

Keywords

Get full access to this article

References