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The Effects of Cognitive Competence and Self Esteem Towards Students' Reading Comprehension agustina ramadhianti
Scope : Journal of English Language Teaching Vol 1, No 2 (2017): Scope: Journal of English Language Teaching
Publisher : Pusat Studi Pendidikan Bahasa dan Sastra Inggris

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.561 KB) | DOI: 10.30998/scope.v1i02.1379

Abstract

The aim of this study is to obtain empirical data and to analyze about whether or not there are the effects of cognitive competence and self esteem towards student’s reading comprehension. The study was conducted at the State Junior High Schools in South Jakarta from November 2012 to January 2013. The research method is survey with multiple-regression analysis technique. The research findings show three points. Firstly, there is a signinificant effect of cognitive competence and self-esteem towards student’s reading comprehension with multiple coefficient correlation is 0,816. Secondly, there is a significant effect of cognitive competence towards student’s reading comprehension since tobserved is higher than ttable (4.728 > 1.669). Thirdly, there is a significant effect of self-esteem towards student’s reading comprehension since tobserved is higher than ttable (6.476 > 1.669). Based on the research findings, the student’s reading comprehension can be developed by increasing the cognitive competence and self-esteem of the students
Governing Artificial Intelligence in Higher Education: Policy Implications for Faculty Professional Competence Santoso, Doni Anggoro Ari; Agustina Ramadhianti; Sugianti Somba; Nandang Hidayat
Candradimuka: Journal of Education Vol. 4 No. 1 (2026): Candradimuka: Journal of Education
Publisher : Pusat Penerjemah dan Penyunting Universitas Indraprasta PGRI

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Abstract

This study investigates how artificial intelligence (AI) is governed in higher education institutions and examines the implications of institutional AI governance policies for faculty professional competence. A qualitative policy analysis approach was employed. Data were collected through document analysis of institutional AI-related policies, including academic integrity guidelines and faculty development regulations, as well as semi-structured interviews with faculty members and academic leaders involved in AI governance and implementation. The data were analyzed using thematic analysis guided by an AI governance framework encompassing pedagogical, governance, and operational dimensions. The findings reveal that higher education institutions have increasingly formalized AI governance through institutional policies. However, these policies are largely regulatory in orientation, with a strong emphasis on ethical compliance, academic integrity, and risk mitigation. Explicit pedagogical guidance and systematic support for faculty professional competence development are limited. As a result, faculty members experience uncertainty in applying AI in teaching and assessment practices and rely predominantly on self-directed or informal learning, leading to uneven levels of AI-related professional competence. The study suggests that AI governance in higher education should move beyond compliance-oriented regulation toward an integrative, capacity-building approach. Institutional AI policies need to be aligned with structured faculty development frameworks that incorporate pedagogical guidance, ethical awareness, and operational support to enable responsible and effective AI integration in academic practice.