Enhancing e-Learning with AI and Blockchain: A Predictive Analysis of Acceptance Factors and Academic Performance
Keywords:
E-Learning, Human-Computer Interaction, Performance ImpactAbstract
The research paper explores the key issues that determine the acceptance of eLearning tools by students and their effect on academic performance. Although the Technology Acceptance Model (TAM) has been used as the main model to describe adoption behavior in the past, little focus has been on the relationship between acceptance and performance outcomes. To fill this gap, we suggest a combined IS-TAM framework and confirm it using two datasets collected from higher education institutions (N = XXX). The results of Structural Equation Modeling (SEM) show that Perceived Ease of Use has α = 1.00 (p < 0.05), Perceived Usefulness (α = 0.8, p < 0.01), and System Quality (α = 0.87, p < 0.01). Moreover, the accuracy of the Machine Learning model was 0.79, indicating good predictive performance based on factors related to acceptance. The findings indicate that acceptance modeling coupled with predictive analytics is a more holistic way of understanding eLearning effectiveness. The study is valuable in that it bridges a gap between the behavioral and performance perspectives, which can be used in practice to enhance the design and student achievement of eLearning systems.
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