An investigation into the Factors Affecting Perceived Enjoyment of Learning in Augmented Reality: A Path Analysis

Document Type: Original Article

Authors

Department of Educational Sciences, Payame Noor University, Tehran, Iran

10.5812/ijvlms.2020.87946.1054

Abstract

Background: Teaching and learning are undergoing a dramatic transformation thanks to the technological advances in areas like Augmented Reality (AR). The main purpose of this study was to investigate the factors affecting perceived enjoyment of learning in AR. Methods: This was an applied research in terms of purpose and a descriptive and correlative study in terms of methodology. The statistical population included all undergraduate students at Payam-e Noor University in western areas of Iran during 2019- 2020 academic year (n=24000). A sample of 600 students were selected through randomized multistage cluster sampling based on Cochran’s formula. The participants used an AR application, and then completed an integrated questionnaire, which was a combination of 5 questionnaires (flow, perceived enjoyment, need for cognition, cognitive absorption and self-efficacy). A total of 556 questionnaires were returned. The data were analyzed through path analysis using Amos 22, Lisrel 8.50 and Spss 22. Results: Among the direct effects, self-efficacy had the highest effect on perceived enjoyment (0.28) and need for cognition had the lowest effect on self-efficacy (0.16). On the other hand, cognitive absorption had the highest indirect effect on perceived enjoyment (0.13) and the lowest indirect effects were those of the need for cognition on flow (0.04) and self-efficacy on flow (0.04). The highest total effect was related to the effect of self-efficacy on perceived enjoyment (0.28) and the lowest one was related to the effect of selfefficacy on flow (0.04). Conclusion: The results obtained for the fit indices of the proposed model showed that it had a good fit with the data collected from the respondents (X2 =22.14, P=0.179, CFI=0.99, GFI=0.99, AGFI=0.98 & RMSEA=0.023). Accordingly, this model can provide educators and education leaders with critical information for improving learning outcomes.

Keywords


Serio AD, Ibanez MB, & Kloos CD. Effect of an augmented reality system on students’ motivation for a visual art course. Computers and education. 2013; 68: 586-596.
Bujak KR, Radu I, Catrambone R, MacIntyre B, Golubski G, & Zheng R. A psychological perspective on augmented reality in the mathematics Classroom. Computer and education.2013; 68: 536-544. https://doi.org/10.1016/j.compedu.2013.02.017
Rauschnabel PA, Brem A, & Ivens, B S. Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables. Computers in Human Behavior. 2015; 49 :635-647.doi: 10.1016/j.chb.2015.03.003
Patrick RPL, Zheng J, Guo Z & Li J. Speed reading on virtual reality and augmented reality. Computers & Education.2018;125:240-245. DOI: 10.1016/j.compedu.2018.06.016
Hwang WY, & Hu SS. Analysis of peer learning behaviors using multiple representations in virtual reality and their impacts on geometry problem solving. Computers & Education. 2013;62: 308–319.https://doi.org/10.1016/j.compedu.2012.10.005
Fransson BA, Chen C Y ,Noyes J A & Ragle CA. Instrument Motion Metrics for Laparoscopic Skills Assessment in Virtual Reality and Augmented Reality. VeterinarSurgery, American College of Veterinary Surgeons. 2016; 1- 9. doi: 10.1111/vsu.12483
Chang HY, Wu HK & Hsu YS. Integrating a mobile augmented reality activity to contextualize student learning of a socioscientific issue. British Journal of Educational Technology. 2013;3: 95-99.https://doi.org/10.1111/j.1467-8535.2012.01379.x
Kesima M, OzarslanbY. Augmented reality in education: current technologies and the potential for education. Procedia - Social and Behavioral Sciences.2012; 47: 297 – 302.
Davis FD, Bagozzi RP & Warshaw PR. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology.1992; 22 (14): 1111-1132.
Mubuke F, Ogenmungu C, Mayoka KG, Masaba AK & Andrew W.  The predictability of perceived enjoyment and its effect on the intention to use mobile learning systems. Asian Journal of Computer Science and Information Technology.2017;7(1):1-7. http://dx.doi.org/10.15520/ajcsit.v6i8.51
Huang, Y. Empirical Analysis on Factors Effecting Mobile Learning Acceptance in Higher Engineering Education[dissertation].[ Tennessee ]:University of Tennessee;2014.
Nguyen D. Understanding Perceived Enjoyment and Continuance Intention in Mobile Games[dissertation].[Aalto]: Aalto University;2015.
Wong WT, & Huang NTN. The effects of eLearning system service quality and users' acceptance on organizational learning. International Journal of Business and Information. 2015; 6(2): 205-225.
Jambulingam M.Behavioral intention to adopt mobile technology among tertiary students. World Applied Sciences Journal. 2013 ;22(9): 1262-1271.
Yi, M. Y., & Hwang, Y. Predicting the use of web-based information systems: Self efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal Human-Computer Studies. 2003;59: 431-449. https://doi.org/10.1016/S1071-5819(03)00114-9
Ahmadi Deh Ghotbadini, M. & Moshkani, M. The Effect of Computer Self-Efficacy and Perceived Enjoyment on Davis, Technology Acceptance Model Constructs. Journal of Psychology. 2011; 15: 58-75.
Gou YM, Ro YK. Capturing Flow in the Business Classroom. Decision Sciences Journal of Innovative Education.2008; 6(2):437-462. DOI: 10.1111/j.1540-4609.2008. 00185.x
WeibelD,  and Wissmath B. Immersion in Computer Games: The Role of Spatial Presence and Flow. International Journal of Computer Games Technology.2011; 2011, Article ID 282345, 14 pages doi:10.1155/2011/282345
Min Huang Y & Hsuan Lin P. Evaluating students’ learning achievement and flow experience with tablet PCs based on AR and tangible technology in u-learning. Library Hi Tech.2017;35(4):602-614.doi: 10.1108/LHT-01-2017-0023
Hatlevik OE, Throndsen I, Loi M & Gudmundsdottir GB. Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education.2018;118:107-119.doi: 10.1016/j.compedu.2017.11.011
Bandura A. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review.1977; 84(2):191-215. doi: 10.1037/0033-295X.84.2.191
Hwang Y, Lee Y & Shin DH. The role of goal awareness and information technology self-efficacy on job satisfaction of healthcare system users. Behavior & Information Technology.2016; 35(7):548-558. doi: 10.1080/0144929X.2016.1171396
Sun JCY & Rueda R. Situational interest, computer self-efficacy and self-regulation: their effect on student engagement in distance education.British Journal of Educational Technology.2012; 43(2):191-204. doi: 10.1111/j.1467-8535.2010.01157.x
Elias, S. M., & Loomis, R. J. Utilizing need for cognition and perceived self-efficacy to predict academic performance. Journal of Applied Social Psychology. 2002.  32(8): 1687-1702.  https://doi.org/10.1111/j.1559-1816.2002.tb02770.x
Weniger, S., Loebbecke, C. Cognitive absorption: literature review and suitability in the context of hedonic IS usage. Department of business, media and technology management, University of Cologne, Germany. 2007. 
Boyle EA, Connolly TM., Hainey T & Boyle JM. Engagement in digital entertainment games: A systematic review. Computers in Human Behavior.2012; 5(28), no.3:771-780 ISSN 0747-5632. DOI:  http://dx.doi.org/10.1016/j.chb.2011.11.020.
Csikszentmihalyi M & Csikszentmihalyi I. Optimal experience. Psychological studies of flow in Consciousness. Cambridge: Cambridge University Press; 1988.
Agarwal R, & Karahanna E. Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly.2000;24(4):665-694.doi: 10.2307/3250951
Huprich, j. Enhancing learner flow and cognitive absorption. 2019; Available from:  https://experience.exceedlms.com/student/activity/453887
Cacioppo JT & Petty RE.The need for cognition. Journal of Personality and Social Psychology.1982; 42:116-131. http://dx.doi.org/10.1037/0022-3514.42.1.116
Petty R E, Brinol P, Loersch C & McCaslin,M J. The need for cognition. In M. R. Leary & R. H. Hoyle(Eds.), Handbook of individual differences in social behavior. New York: Guilford Press;2009.
Negahdari S, Seif MH, Farajollahi M & Rastegar A. Providing a Causal Model of Perceived Learning on the Basis of Digital Games. Quarterly Journal of Research in School and Virtual Learning. 2018;21(1): 105-119.
Li, Dahui and Browne, Glenn. The Role of Need for Cognition in Online Flow Experience: An Empirical Investigation. 2004; AMCIS, Proceedings.386.
Cacioppo JT, Petty RE & Kao C F. The efficient assessment of need for cognition. Journal of Personality Assessment.1984; 48: 306-307. http://dx.doi.org/10.1207/s15327752jpa4803_13
Sadowski CJ. An examination of the short need for cognition scale. Journal of Psychology.1993; 127:451-454. http://dx.doi.org/10.1080/00223980.1993.9915581
Rastegar, A. Presenting a Causal Model of Relationships between Need for Cognition andCognitive Engagement With Emphasis on the Mediating Role of Achievement Goals and Academic Emotion. Social Cognition. 2017;6(1): 8-26.
Kazuki, Y., Asakawa, K., Taro, Y., Satoshi, S., Daisuke, S., Yui, M. The Flow State Scale for Occupational Tasks: Development, Reliability, and Validity. Hong Kong Journal of Occupational Therapy. 2013;23: 54-61. http://dx.doi.org/10.1016/j.hkjot.2013.09.002
Reychav, I., Dezhi, W. Are your users actively involved? A cognitive absorption perspective in mobile training. Computers in Human Behavior. 2015; 44 :335–346. http://dx.doi.org/10.1016/j.chb.2014.09.021
Mahat, Mohd Ayub AF & Wong SL. An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Social and behavioral sciences.2012; 64:284-290. https://doi.org/10.1016/j.sbspro.2012.11.033
Elias SM. & Loomis RJ. Utilizing Need for Cognition and Perceived Self-Efficacy to Predict Academic Performance. Journal of Applied Social Psychology.2002; 32(8): 1687-1 702. doi: 10.1111/j.1559-1816.2002.tb02770.x