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All Journal Journal of Applied Data Sciences Jurnal Pendidikan Ekonomi, Perkantoran, dan Akuntansi
Nova Syafira Ariyanti
Faculty of Education, Universitas Negeri Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences Other

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Unveiling the Pathways from Parenting to Entrepreneurship: A Structural Equation Modeling Approach Maulana Amirul Adha; Nova Syafira Ariyanti; Ferry Setyadi Atmadja; Mayang Riyantie; Nina Farliana; Rudy Ansar
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1031

Abstract

This study investigates how parenting styles influence vocational students’ entrepreneurial intentions and career choices, considering self-efficacy and entrepreneurial attitudes as mediating variables. Using a quantitative approach, data were collected from 381 vocational school students and analyzed with Structural Equation Modeling (SEM) using AMOS 24. The participants consisted of 55.7% females and 44.3% males, representing families from low-, middle-, and high-income groups based on the 2024 Jakarta provincial minimum wage, with parents working as civil servants, private-sector employees, entrepreneurs, and others. The results indicate that authoritative parenting positively fosters entrepreneurial intentions and encourages students to pursue entrepreneurship as a career path. Furthermore, the mediating roles of self-efficacy and entrepreneurial attitudes are confirmed, providing a clearer explanation of how parenting influences entrepreneurial career decisions. The study contributes theoretically by extending models of entrepreneurial intention with family socialization factors, and practically by offering a tested framework to guide efforts in promoting entrepreneurship among vocational students.
ARTIFICIAL INTELLIGENCE IN ENHANCING LEARNING MOTIVATION AND SELF-DIRECTED LEARNING AMONG PRE-SERVICE TEACHERS: A SYSTEMATIC REVIEW Nova Syafira Ariyanti; Maisyaroh Maisyaroh; Indra Lesmana; Maulana Paramaditya Ananta
Jurnal Pendidikan Ekonomi, Perkantoran, dan Akuntansi Vol. 6 No. 3 (2025): Jurnal Pendidikan Ekonomi, Perkantoran, dan Akuntansi
Publisher : Faculty of Economics and Business, Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jpepa.0603.07

Abstract

This study aims to analyse the use of Artificial Intelligence (AI) in improving student teacher motivation and learning independence with a Systematic Literature Review (SLR) approach. The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with data sources from the Scopus database for the period 2019–2024. The process of article selection was done through the phases of identification, screening, testing of feasibility and selection of final article. This process resulted in 32 articles being retrieved that met the inclusion criteria and were analysed using thematic analysis techniques. Results of this research indicate that the use of AI, especially generative artificial intelligence such as ChatGPT, learning chatbots, and intelligent tutoring systems, could improve student learning motivation through more flexible, interactive, and personalised learning. In addition, AI also enables enhanced self-directed learning through easy access to information, self-management of the learning process, and the development of problem-solving skills. This study contributes to the growing body of research on the implementation of AI in higher education and specifically the digital learning transformation for student teachers in the era of modern education.
Co-Authors Ferry Setyadi Atmadja Indra Lesmana Maisyaroh Maulana Amirul Adha Maulana Paramaditya Ananta Mayang Riyantie Nina Farliana Rudy Ansar