Digital Learning Transformation, Deep Learning Pedagogy, and Teacher Professionalism: Effects on Primary School Students’ Motivation

Abstract

This study is motivated by the challenges of educational transformation in the digital era, where sixth-grade students’ learning motivation often declines due to conventional teaching methods and academic pressure prior to graduation. In Cluster IV of Dongko District, the integration of educational digitalization, deep learning principles, and teacher professional competence is considered essential for creating a supportive learning ecosystem. This research aims to analyze the effects of learning digitalization, deep learning principles, and teachers’ professional competence on students’ learning motivation, both partially and simultaneously. A quantitative research design with an explanatory approach was employed. The population consisted of all sixth-grade students in elementary schools within Cluster IV of Dongko District, with a sample of 120 respondents selected through quota sampling. Data were collected using validated and reliable questionnaires. Data analysis techniques included classical assumption tests, multiple linear regression analysis, and hypothesis testing (t-test and F-test) using SPSS. The results indicate that learning digitalization has a positive and significant effect on learning motivation (t = 2.622). Deep learning principles emerged as the most dominant variable influencing learning motivation (t = 6.790). Teacher professional competence also showed a significant partial effect. Simultaneously, the three variables exert a strong and significant influence on students’ learning motivation (F = 190.797; p = 0.000), demonstrating that the synergy between digital tools, deep learning pedagogy, and teacher competence is a key driver of student motivation.