Interdisciplinary Journal of Virtual Learning in Medical Sciences (Dec 2020)

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

  • Maryam Darvishi,
  • Mohammad Hassan Seif,
  • Mohammad Reza Sarmadi,
  • Mehran Farajollahi

DOI
https://doi.org/10.5812/ijvlms.2020.87946.1054
Journal volume & issue
Vol. 11, no. 4
pp. 224 – 235

Abstract

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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.

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