Measuring the Effect of E-Learning Information Quality on Student’s Satisfaction Using the Technology Acceptance Model

Huda Aljader - Middle Technical University (MTU), Baghdad, Iraq


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DOI: http://dx.doi.org/10.30630/joiv.7.3.1633

Abstract


This study analyses a blended e-learning system's information resources. Their quality is assessed based on learners' perceptions using a modified version of the Technology Acceptance Model (TAM). To enable flexible learning and enhance understanding during the COVID-19 epidemic, most Iraqi universities have lately embraced Google Classroom and Moodle in addition to face-to-face (F2F) courses. Based on TAM, individual differences and perspectives were investigated concerning correlations between student satisfaction and technology adoption. There were 270 undergraduate students in the research sample who were enrolled in academic courses at Middle Technical University's (MTU) /Technical College of Management (TCM). A survey was used for data collection. The research was done after developing the model's essential and external variables and selecting their components. Partial least squares structural equation modelling (PLS-SEM) examined path-connected dependent and independent components. The study's results showed how "E-Learning Information Quality" (EIQ) positively impacted students' adoption of e-learning. That is demonstrated by the internal variables' positive correlation, which includes perceived usefulness (PU) and perceived ease of use (PEOU), which can be seen in H1 and H2 by the values of (β = 0.204, β = 0.715), and which both positively influence attitudes toward use (ATU), which can be seen in H5 were value (β = 0.643), and behavioral intention (BIU), which can be seen in H4 was value (β = 0.300). Therefore, e-Learning information sources must have value and meaning for students. However, more research is required to evaluate the system's quality. Furthermore, the acceptability of e-learning may change as pedagogies change


Keywords


TAM; e-Learning; Structural Equation Modeling; PLS-SEM.

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