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Journal : science information system and technology

The Influence of Equitable Instructional Practices and Teachers’ Pedagogical Competence on Student Learning Engagement in Inclusive Schools in Indonesia Istiarsyah, Istiarsyah; Devita, Dela; Astuti, Eka Yuli; Hayati, Pelita; Rafika, Irda
West Science Information System and Technology Vol. 4 No. 01 (2026): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v4i01.2855

Abstract

This study investigates the effect of equitable teaching practices and teachers' pedagogical competence on student engagement in inclusive schools in Indonesia. A quantitative approach was adopted, with data collected from 225 respondents using a Likert-scale survey. The study examines the relationships between equitable teaching practices, pedagogical competence, and student engagement in inclusive classrooms. Data analysis was conducted using SPSS version 25, including descriptive statistics, correlation analysis, and regression analysis. The findings indicate that both equitable teaching practices and teachers' pedagogical competence significantly predict student engagement, with equitable teaching practices having a stronger influence. The study suggests that inclusive teaching strategies and enhancing teachers’ pedagogical competence are essential for fostering student engagement in inclusive classrooms. These findings provide valuable insights for educators and policymakers working to improve inclusive education practices in Indonesia.
Analysis of Technology and Organizational Readiness for Cloud-Based LMS Implementation in Higher Education Laila Qadriah; Istiarsyah Istiarsyah
West Science Information System and Technology Vol. 4 No. 02 (2026): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v4i02.3061

Abstract

This study examines the technological and organizational readiness of higher education institutions in Indonesia for the implementation of a cloud-based Learning Management System (LMS). A quantitative approach with a cross-sectional survey design was employed involving 155 respondents consisting of lecturers, academic and administrative staff, information technology personnel, and academic managers. Data were collected using a structured questionnaire measured on a five-point Likert scale and analyzed using IBM SPSS Statistics version 25. The analysis included descriptive statistics, validity and reliability tests, classical assumption tests, Pearson correlation, and multiple linear regression. The results indicate that technological readiness was categorized as high, with a mean score of 4.03, while organizational readiness recorded a mean score of 3.91. Overall cloud-based LMS implementation readiness also reached a high level, with a mean score of 3.98. Technological readiness had a positive and significant effect on LMS implementation readiness, while organizational readiness demonstrated a stronger significant effect. Together, both dimensions explained 61.5% of the variance in implementation readiness (R² = 0.615). These findings indicate that successful cloud-based LMS implementation requires not only adequate technological infrastructure but also strong managerial commitment, institutional policies, human resources, training, financial support, and inter-unit coordination.
Are Synthetic Data and Privacy Protection the Future of Artificial Intelligence Development? Istiarsyah Istiarsyah; Tina Isnaeni; Rival Pahrijal
West Science Information System and Technology Vol. 4 No. 02 (2026): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v4i02.3091

Abstract

The rapid advancement of Artificial Intelligence (AI) has created increasing dependence on large-scale datasets, while simultaneously generating significant legal challenges related to privacy protection, data governance, and individual rights. This study examines whether synthetic data and privacy protection mechanisms can become the future foundation of responsible AI development through a normative legal analysis approach. The research analyzes relevant legal frameworks, regulatory principles, and conceptual developments concerning personal data protection, AI governance, and the utilization of synthetic data as a privacy-preserving alternative. The findings indicate that synthetic data provides substantial potential to reduce privacy risks by minimizing direct exposure to identifiable personal information while improving data accessibility for AI training and innovation. However, synthetic data does not automatically eliminate legal concerns, particularly regarding re-identification risks, accountability allocation, transparency, and regulatory uncertainty. The analysis demonstrates that effective AI governance requires a shift from traditional data protection approaches toward adaptive frameworks based on risk assessment, privacy-by-design principles, and responsible technology development. The study argues that synthetic data should not be viewed as a complete replacement for real-world data but as a complementary mechanism within a broader privacy-preserving AI ecosystem. Therefore, the future of artificial intelligence development depends on the integration of technological innovation and legally enforceable privacy protection frameworks that ensure transparency, accountability, and respect for fundamental rights.