Rinto Alexandro
Universitas Palangka Raya, Indonesia

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The Effect of Artificial Intelligence (AI), Mobile Phones, and Teaching Models on Student Academic Achievement at SMAN 5 Palangka Raya Tiurma Sari Dewi Flora Sinaga; Rinto Alexandro; Fendy Hariatama
Journal of Education and Teaching Learning Vol 8 No 1 (2026): Journal of Education and Teaching Learning (JETL)
Publisher : CV. Pusdikra Mitra Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51178/jetl.v8i1.3326

Abstract

This study aims to analyze the influence of Artificial Intelligence (AI) utilization, mobile phone usage, and teacher learning models on student academic achievement in Economics at SMAN 5 Palangka Raya. Using a quantitative associative approach with a sample of 123 students, data were collected through valid and reliable questionnaires with a Cronbach’s Alpha of 0.887. The utilization of Artificial Intelligence (AI) in learning is closely associated with the use of mobile phones as the primary medium for students to access technology. Multiple linear regression analysis results show that the three variables simultaneously have a significant effect on academic achievement, contributing 98.9% (R2 = 0.989). Partially, AI (B= 0.434), mobile phones (B= 0.396), and teacher learning models (B = 0.499) have positive and significant effects with a significance of 0.000. The teacher learning model was found to be the most dominant factor. This suggests that the pedagogical strategies implemented by teachers play a central role in determining students' academic success. In conclusion, the integration of digital technology supported by adaptive pedagogical strategies is highly effective in enhancing students' academic performance in the digital era. While the integration of digital tools significantly boosts academic performance, pedagogical mastery remains the most critical factor in student success. The findings are expected to provide empirical evidence for schools and teachers in designing relevant and effective learning strategies in the digital era.
Core or periphery? Sustainability signals and Green HRM at UPR Florensius Yantriantho; Meitiana Meitiana; Rinto Alexandro; Ronni Haga; Sunaryo Neneng
Journal of Economics, Entrepreneurship, Management Business and Accounting Vol 4 No 5 (2026): Volume 4, Issue 5, September 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jeemba.v4i5.1976

Abstract

  Purpose – Universities may display strong sustainability identities without embedding environmental priorities in their employment systems. This study examines whether Universitas Palangka Raya’s (UPR) public sustainability signals are reflected in employee-perceived Green HRM system strength, green psychological climate, and organisational citizenship behaviour for the environment (OCBE). Design/methodology/approach – A sequential single-case design combined a structured audit of 29 official documents from 11 UPR units with a cross-sectional employee survey (n = 250). The survey model was estimated using PLS-SEM, representing system strength through distinctiveness, consistency, and consensus, green psychological climate as mediator, and OCBE through eco-civic engagement, eco-helping, and eco-initiatives. Finding/Results – The audit identified strong environmental knowledge, infrastructure, and stakeholder engagement but weak public codification of recruitment, appraisal, and reward practices. System strength was positively associated with green psychological climate (β = 0.556, p < .001) and OCBE (β = 0.220, p < .001), while green psychological climate was positively associated with OCBE (β = 0.439, p < .001). The indirect effect was significant (β = 0.244, p < .001), indicating complementary partial mediation. Originality/Value – The study connects institution-level digital evidence with employee-level process perceptions in a single higher-education case. UPR can strengthen its system by formalising green competencies, development, role-relevant performance criteria, recognition, and transparent communication.