Investigating Student Satisfaction and Adoption of Technology-Enhanced Learning to Improve Educational Outcomes in Saudi Higher Education

Abstract

The current tendency in education is to deliver high-quality instruction with the use of technology in order to increase students’ global competitiveness. Currently, few empirical studies in the literature examine the significance and implications of technology-enhanced learning (TEL) in Saudi higher education. Therefore, the current study aims to develop a new model by examining the impact of a number of variables, including students’ perceived support, virtual social skills, subjective norms, information quality, subjective interest, and resource availability, on students’ self-efficacy and students’ perceived benefits of TEL enrolled in various public and private educational institutions in Saudi Arabia. The method is based on partial least squares structural equation modeling (PLS-SEM). A survey question on the idea of students’ self-efficacy and students’ perceived benefits of TEL was used as the main method of data collection, and 274 valid responses from undergraduate and graduate students at King Faisal University, particularly those who have been using TEL for at least a year, were obtained. The results of the student self-efficacy survey show that TEL adoption and student satisfaction are both positively impacted. The finding of this study was that all of the TEL characteristics were significantly and favorably mediated by perceptions of the TEL benefits. Student satisfaction is influenced by students’ perceptions of support, virtual social skills, subjective norms, informational quality, subjective interest, resource accessibility, and TEL uptake by students.

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“…The minimum was the AVE value of PU at 0.689, and the maximum was the AVE value of IAD at 0.917. Furthermore, the degree to which a concept differs empirically from other constructs is referred to as discriminant validity [46]. To ensure adequate discriminant validity, three key criteria proposed by Leguina [47] were applied.…”

“…PLS-SEM does not generate model fit statistics, but it does use the coefficient of determination (R 2 ) in the dependent constructs to assess the explanatory power of a structural model [50]. The R 2 indicates the percentage of exogenous variables that may be able to predict the endogenous variables [46]. R 2 values can describe the level of predictive accuracy [51]: values of 0.25 and less are weak, values between 0.25 and 0.75 are moderate, and values exceeding 0.75 are strong [52].…”