An integrated model exploring the relationship between self-efficacy, technology integration via Blackboard, English proficiency, and Saudi EFL students’ academic achievement

Mohammad H. Al-khresheh () and Taha O. Alkursheh
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Mohammad H. Al-khresheh: Northern Border University
Taha O. Alkursheh: University of Tabuk

Palgrave Communications, 2024, vol. 11, issue 1, 1-12

Abstract: Abstract The proliferation of technology in educational settings and its impact on learning outcomes has become a focal point in educational research. In language education, the interplay among technological tools, learner self-efficacy, and language proficiency is critical for academic success. This study aims to shed light on these dynamics by presenting a comprehensive structural model that elucidates the relationships and causal effects among students’ academic achievement, English proficiency, self-efficacy, and the utilization of instructional technology while focusing on the Blackboard learning management system. Employing a quantitative correlational design, this study used three questionnaires to measure the primary variables. The study sample included 590 university students from two universities purposively selected using random stratified sampling to ensure representativeness. Statistical analyses—including descriptive statistics, correlation coefficients, and structural equation modeling (path analysis)—were employed to investigate the data. The emergent model demonstrated a perfect fit to the sample data, exhibiting robust goodness-of-fit indicators. The findings highlight the direct positive influence of self-efficacy on academic achievement and the beneficial effects of Blackboard integration on English proficiency and academic success. These insights emphasize the importance of self-efficacy in educational achievement and the pivotal role of e-learning platforms in enhancing students’ motivation and linguistic skills. The implications of these results are profound, suggesting avenues for future research to examine the applicability of the structural model across diverse educational contexts and incorporate additional variables for a more granular understanding of the factors driving academic achievement in technology-enhanced learning environments.

Date: 2024
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DOI: 10.1057/s41599-024-02783-2

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