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All Journal Journal of Advanced Sciences and Mathematics Education
Afandi
Universitas Tanjungpura, Indonesia
Chemistry Education Mathematics Physics Social Sciences

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Exploring the association between deep learning approach, problem-solving skills, digital literacy, and student learning outcomes: a data-driven nonparametric study in science education Eviana; Achmadi; Afandi
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 2 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i2.1188

Abstract

Background: The demands of 21st-century education require students to possess higher-order thinking skills, particularly problem-solving abilities, along with adequate digital literacy and optimal academic achievement. However, these competencies are often underdeveloped due to the continued use of surface-level instructional approaches that limit meaningful learning engagement. Aims: This study aims to examine the association between the deep learning approach, problem-solving skills, digital literacy, and student learning outcomes, as well as to provide empirical evidence of the interrelationships among these variables in the context of science education. Method: A quantitative correlational design was employed involving 121 tenth-grade students selected through proportionate random sampling from a population of 199 students. Data were collected using validated questionnaires measuring the deep learning approach, problem-solving skills, digital literacy, and learning outcomes. Prior to analysis, validity and reliability tests were conducted. Due to the non-normal distribution of one variable, Spearman’s Rho correlation analysis was applied. Results: The findings revealed significant positive correlations between the deep learning approach and problem-solving skills, as well as digital literacy, both indicating moderate relationships. A weaker but statistically significant correlation was found between the deep learning approach and learning outcomes. Additionally, problem-solving skills and digital literacy demonstrated the strongest relationship among the variables. Conclusion: These findings suggest that the deep learning approach is closely associated with the development of higher-order thinking and digital competencies, although its direct relationship with learning outcomes is limited. Therefore, integrating deep learning strategies with problem-solving and digital literacy activities is essential to enhance students’ readiness for 21st-century learning.
Urban–Rural differences in teachers’ acceptance of artificial intelligence for teaching and learning: Evidence from indonesia using the technology acceptance model Lusiwati Iriani Butar-Butar; Afandi; Reni Marlina; Eny Enawaty; Eva Faja Ripanti
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 2 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i2.1280

Abstract

Background: Artificial Intelligence (AI) offers significant opportunities to enhance teaching and learning through instructional support, automated assessment, and learning analytics. However, teachers’ acceptance of AI may vary according to perceived usefulness, ease of use, and differences in technological access across geographical settings. Aim: This study aimed to compare teachers’ acceptance of AI for teaching and learning between urban and rural schools in Melawi Regency, Indonesia. Method: A quantitative comparative design was employed using the Technology Acceptance Model (TAM), encompassing Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioral Intention (BI). Data were collected from 100 teachers through an online Likert-scale questionnaire. Content validity was established through expert review, and the data were analyzed using descriptive statistics and an independent samples t-test. Results: The findings indicated that teachers in both settings demonstrated high levels of AI acceptance. However, urban teachers reported higher acceptance (M = 4.22) than rural teachers (M = 4.05). The difference was statistically significant (t = 7.35, p < 0.001) with a very large effect size (Cohen’s d = 2.32), suggesting a substantial influence of geographical context on AI acceptance. Conclusion: Urban teachers exhibit significantly greater acceptance of AI than rural teachers. Infrastructure availability, digital literacy, and institutional support appear to be key factors influencing this disparity. Strengthening digital capacity and improving technological infrastructure are essential to promote equitable AI integration in education.
Co-Authors Achmadi Eny Enawaty Eva Faja Ripanti Eviana Lusiwati Iriani Butar-Butar Reni Marlina