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La Ode Achmad Suherman
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APLIKATIF: Journal of Research Trends in Social Sciences and Humanities
Published by Lembaga Riset Dan Inovasi Masyarakat Madani
ISSN : 29628342     EISSN : 29627222     DOI : https://doi.org/10.59110/aplikatif
Core Subject : Economy, Education, Art, Social,
Arts Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences
APLIKATIF: Journal of Research Trends in Social Sciences and Humanities seeks Review articles, Case reports and original contributions in the area of Social Science and Humanities. The Journal invites original Research Papers, Review articles, Technical or Case reports and Short communications that are not published or not being considered for publication. This journal utilizes the LOCKSS system to create a distributed archiving system among participating libraries and permits those libraries to create permanent archives of the journal for purposes of preservation and restoration.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 142 Documents
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Generative AI and Self-Regulated Learning in Higher Education: A Narrative Review with Implications for Physics Education Abdul Walid; Ishak Ishak; Andi Kamal Ahmad; Juniar Rasyid
APLIKATIF: Journal of Research Trends in Social Sciences and Humanities Vol. 4 No. 3 (2026): APLIKATIF: Journal of Research Trends in Social Sciences and Humanities
Publisher : Lembaga Junal dan Publikasi, Universitas Muhammadiyah Buton

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/aplikatif.968

Abstract

The rapid development of generative artificial intelligence (generative AI) is reshaping how students access information, complete academic tasks, and regulate learning in higher education. This narrative literature review synthesizes and critically interprets evidence on the role of generative AI in learning strategies and learning independence, with provisional implications for physics education. Searches were conducted in SINTA, Scopus, Web of Science, ScienceDirect, and SpringerLink for publications from 2020 to 2026, with Google Scholar used as a supplementary source. Twelve peer-reviewed publications were included in the main thematic synthesis and interpreted through Zimmerman’s self-regulated learning framework, Bandura’s social cognitive theory, and cognitive load theory as an additional perspective. The findings indicate that generative AI may support goal setting, feedback, motivation, strategy development, self-efficacy, and reflection. However, its benefits depend on active engagement, self-regulation capacity, output quality, and metacognitive guidance. Unguided use may encourage cognitive offloading, weaken self-monitoring, and improve task performance without equivalent conceptual understanding. Because only one study directly involved university students in physics or physics education, the implications remain provisional. Physics education programs should therefore promote independent problem solving, critical evaluation, transparent AI use, and structured metacognitive guidance.
Critical Netizen Perceptions of Polri in Instagram Discourse on the Affan Kurniawan Case: A CMDA-Appraisal Study Enlynia Retno; Faris Annas
APLIKATIF: Journal of Research Trends in Social Sciences and Humanities Vol. 5 No. 2 (2026): APLIKATIF: Journal of Research Trends in Social Sciences and Humanities
Publisher : Lembaga Junal dan Publikasi, Universitas Muhammadiyah Buton

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/aplikatif.970

Abstract

Social media comment sections provide spaces where users evaluate public institutions and negotiate institutional legitimacy. This study examines how critical perceptions of the Indonesian National Police (Polri) were expressed in comments on an @narasinewsroom Instagram post concerning the death of Affan Kurniawan. Using qualitative descriptive content analysis, the study applied quota purposive sampling to select 100 comments from 4,581 available comments, with 20 comments drawn from each of five perception categories. The comments were coded according to their perception category, primary Computer-Mediated Discourse Analysis-informed message form, and primary emotional-evaluative stance, and were subsequently interpreted through Appraisal Theory. Within the quota-balanced sample, questions and demands for transparency were the most frequently coded message form (27%), followed by figurative language, humor, and satire (25%), assertive and evaluative statements (22%), collective statements and calls for action (20%), and argumentative reasoning (6%). Cynicism was the most frequently coded emotional-evaluative stance (45%), followed by suspicion (25%), anger (18%), disappointment (10%), and empathy (2%). The Appraisal analysis showed that the comments combined Affect, Judgement, and Appreciation to evaluate police actors, institutional conduct, and the credibility of the investigation. The study demonstrates how critical institutional perceptions were linguistically and evaluatively constructed within a selected digital discourse corpus. Because the sample was deliberately balanced, the findings describe only the analyzed comments and should not be generalized to all comments on the post or to broader public opinion.

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