Session Information
16 SES 14 A, Online and Blended Learning
Paper Session
Contribution
Online pedagogical practices highlight their potential in improving availability and inclusiveness, especially for individuals with atypical needs (Khan et al., 2022). In this respect, adults comprise the largest audience for online distance education, since the latter provides an opportunity for flexible and continuous learning (Moore & Kearsley, 2011). Still, there exists factors challenging them to engage in online educational; female adult learners have been found to be an especially vulnerable subset of this population (Kara et al., 2019). Individual acceptance and usage of new technologies can be studied using the Technology Acceptance Model (TAM; Davies et al., 1989). According to the TAM, the two key factors in determining the users’ attitudes towards an e-learning system, and consequently, the actual system use, are perceived usefulness (PU) and perceived ease of use (PEOU). Perceived Usefulness (PU) is an individual’s view that the use of a specific system can enhance work performance (Liaw & Huang, 2013). Perceived Ease of Use (PEOU) is the extent to which an individual believes the use of a certain technology system will not require so much effort to be achieved.
The present study evaluates the validity of TAM in the context of e-learning adoption of adult female postgraduate students in a higher education distance learning course in quantitative research methods. We investigate whether PU and PEOU predict users' overall satisfaction with the system's usage. Furthermore, we explore whether students' Computer Anxiety has an effect on PU and PEOU. Importantly, we test whether students' Academic Self-Efficacy can be explained by the two factors underlying the e-learning adoption, PU and PEOU. In this respect, we propose that, in addition to outcomes related to the user experience, namely, Satisfaction from the use of LMS, affective outcomes, namely Academic Self-Efficacy, may also be explained be external factors using the TAM framework. We investigate the direct effect of Computer Anxiety on learners' Academic Self-Efficacy and the indirect effect through PEOU and PU. Our hypothesis is that the effect of Computer Anxiety on ASE will be fully mediated by the two main factors of TAM, namely PU and PEOU. In our models, we control for the perceived quality of the Technical Support for the use of the LMS.
Method
Methods Data Sample The present study uses cross-sectional survey data from a sample of 430 first-year postgraduate students at a Distance Learning program of a private university in Cyprus. The data were collected as part of a quantitative methods course, with a focus on survey research. Our sample consisted mainly of women (371, 85.5%), but there was a very small proportion of men, as well (59 men, 13.6%). Given the focus of our analysis, we decided to listwise exclude men from our sample. The mean age of our participants was 30.46 years old (Mean = 30.46,S.D.=7), with the minimum age being 22 years old, and the maximum 54 years of age. The vast majority of our participants came from Greece (423, 97.5%), while only four came from Cyprus (1%), and two (.5%) from elsewhere. Notable, more than half of our sample were working full-time (264 participants, 60.8%), 88 (20.3%) were working part-time, and 82, 18.9% were not working at all. Measures The two key factors that are present in all studies using the TAM model is Perceived Usefulness (PU) and PEOU (Perceived Ease of Use); these were measured by scales proposed by Sanchéz & Hueros (2010), appropriately adopted and translated in the Greek language. Technology Support scale was also taken from the same study. Perceived Satisfaction and Computer Anxiety were taken from Liaw and Huang (2013). Academic Self-Efficacy was assessed using the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991). Procedures The data were collected during two consecutive semesters (Fall/Spring) using an online questionnaire that was administered to all students of a graduate distance learning course on designing and contacting survey research. Ethical approval for the conduction of this study was obtained from the Cyprus Bioethical Committee. Statistical Analysis We used Structural Equation Modelling (SEM) and Mplus Statistical package (Muthén & Muthén, 2017) to answer our research questions. Before mapping the causal relationships assumed between our contrasts, we verified the construct validity of the scales using Confirmatory Factor Analysis (CFA). Treatment of missing data in our sample involved the use of the default approach in Mplus, namely Full Information Maximum Likelihood (FIML; Lee & Shi, 2021). For assessing model fit we used sample size independent fit indices (Marsh et al., 2015): The Tucker-Lewis and Comparative Fit Indices, TLI and CFI respectively, and the Root-Mean-Square Error of Approximation (RMSEA).
Expected Outcomes
Results/Conclusions Confirmatory Factor Analysis verified the assumed latent structure of our measures, and, overall our analysis verified the TAM. In extending the TAM framework, we modelled Academic Self-Efficacy (ASE) as another outcome in our model and we considered its relationship with the two main factors underlying TAM and technology adoption, namely Perceived Usefulness and Perceived Ease of Use. Both of them positively predicted ASE; their effects though were substantially smaller than the corresponding effects of Satisfaction. In considering the effect of Computer Anxiety on ASE, we considered both the direct effect and indirect effects through Perceived Usefulness and Perceived Ease of Use. However, the former was not statistically significant (β = .011,SE=.046) and was therefore not kept in the final model. Does Technical Support Compensate for the Negative Effect of Computer Anxiety? In our structural model, we assumed a one-directional relationship between computer anxiety and technical support, modelling a causal path from the former to the latter (Figure 1). Thus, we considered the indirect effects of Computer Anxiety on Perceived Usefulness and Perceived Ease of Use through Technical Support. Estimates were both positive and statistically significant. The total effect of Computer Anxiety on Perceived Ease of Use and Perceived Usefulness is estimated as the sum of direct (β = -.519, SE = .049; β=-.303, SE= .068, respectively) and indirect effects (β = .138, SE =.03; β = .089, SE=.026, respectively). Thus, we conclude that higher perceived quality of Technical Support contributes to the decrease of the negative effect of computer anxiety on the two factors (RH6). In spite of this, it does not lead to the total elimination of this effect.
References
References Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. 10.2307/249008 Kara, M., Erdogdu, F., Kokoç, M. and Cagiltay, K., 2019. Challenges faced by adult learners in online distance education: A literature review. Open Praxis, 11(1), pp.5-22. https://doi.org/10.5944/openpraxis.11.1.929 Khan, S., Kambris, M. E. K., & Alfalahi, H. (2022). Perspectives of University Students and Faculty on remote education experiences during COVID-19- a qualitative study. Education and Information Technologies, 27, 4141-4169. 10.1007/s10639-021-10784-w Liaw, S., & Huang, H. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. 10.1016/j.compedu.2012.07.015 Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education, 14, 129-135. 10.1016/j.iheduc.2010.10.001 Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Authors. Pintrich, P.R., Smith, D.A.F., García, T., & McKeachie, W.J. (1991). A manual for the use of the motivated strategies questionnaire (MSLQ). Ann Arbor, MI University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning. Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640. 10.1016/j.chb.2010.06.011
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