E-Learning, Self-Efficacy, and Motivation: Their Influence on Critical Thinking in IPAS Learning

Shohibi Arif; Lilik Sri Hariani; Ari Brihandhono

doi:10.58421/gehu.v4i4.737

DOI:

https://doi.org/10.58421/gehu.v4i4.737

Authors

Shohibi Arif Universitas PGRI Kanjuruhan Malang
Lilik Sri Hariani Universitas PGRI Kanjuruhan Malang
Ari Brihandhono Universitas PGRI Kanjuruhan Malang

Keywords:

E-Learning, Self-Efficacy, Learning Motivation, Critical Thinking

Abstract

21st-century education requires students not only to master knowledge but also to possess critical, creative, communicative, and collaborative thinking skills (4C skills) to address global challenges. This quantitative correlational study examines the impact of e-learning, self-efficacy, and learning motivation on critical thinking skills in elementary science. The research was conducted with all 63 fourth-grade students at SDN Wonoasih 2 Probolinggo. The research instrument was deemed valid and reliable, and the data were analyzed using classical assumption tests, including the Kolmogorov–Smirnov test for normality, linearity, multicollinearity, and heteroscedasticity. This was followed by t-tests, F-tests, and determination coefficients (R²). The analysis revealed a significant collective impact of e-learning, self-efficacy, and learning motivation on critical thinking. Motivation emerged as the most dominant contributing factor, with self-efficacy and e-learning exhibiting subsequently lesser, yet still significant, influence. Simultaneously, the three variables explained 46.5% of the variation in critical thinking ability, while the remaining factors influenced the remaining variation. These findings underscore the importance of integrating problem-solving-based e-learning, enhancing self-efficacy through constructive feedback, and promoting motivation for relevance-based learning and autonomy. The following study suggested adding other variables, such as learning style, family support, and infrastructure readiness, by using a qualitative or mixed-methods approach, and expanding the sample across different levels and regions to improve generalizability.

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