Indica Yona Okyranida, Indica Yona
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Journal : Navigation Physics : Journal of Physics Education

Implementasi Pendekatan Gamifikasi dalam Pembelajaran Fisika: Analisis Bibliometrik Astuti, Irnin Agustina Dwi; Bhakti, Yoga Budi; Okyranida, Indica Yona; Prasetya, Rendi
Navigation Physics : Journal of Physics Education Vol 6, No 1 (2024): Navigation Physics : Journal of Physics Education Vol. 6 No. 1 Tahun 2024
Publisher : UNIVERSITAS INDRAPRASTA PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/npjpe.v6i1.3930

Abstract

To enhance problem-solving skills in physics education, a learning model strategy is needed that can effectively visualize physics concepts and promote interactive learning. One of the learning approaches that can be utilized is gamification. This study aims to analyze the implementation of gamification in physics education using a bibliometric approach with the VOSViewer software. The method used in this study is bibliometric analysis. There are 5 classification clusters identified through the VOSViewer software. The results of the study show that articles using the keywords "physics education" and "gamification" have significantly increased in 2020, 2021, and 2022. From the analysis using VOSViewer, the words "physics" and "gamification" frequently appeared, indicating that there has been considerable research on gamification. However, it becomes an innovation when linked to physics education, both in terms of teaching and learning media
Deep Learning in Physics Education: Exploring the Potential of Mindful, Meaningful, and Joyful for a Better Learning Experience Sumarni, Ria Asep; Okyranida, Indica Yona
Navigation Physics : Journal of Physics Education Vol 7, No 1 (2025): Navigation Physics : Journal of Physics Education Vol. 7 No. 1 Tahun 2025
Publisher : UNIVERSITAS INDRAPRASTA PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/npjpe.v7i1.4215

Abstract

This article explores the synergistic potential of integrating the Deep Learning  approach with the Mindful, Meaningful, and Joyful learning paradigm to enhance physics education. Traditional physics instruction often faces "fundamental barriers" in human learning, leading to a lack of student engagement and a decline in expert-like confidence. Despite efforts to improve teaching methods, significant progress in student learning outcomes remains difficult to achieve. We argue that Deep Learning , with Intelligent Tutoring System (ITS) capabilities in personalization, adaptation, and interactive simulation, can act as a powerful driver to foster mindful, meaningful, and joyful learning experiences in physics. Mindful learning enhances cognitive and emotional well-being, meaningful learning promotes deep understanding and relevance, and joyful learning nurtures intrinsic motivation and creativity.  A comprehensive review of the latest literature (2015-2025) reveals that intelligent Deep Learning -powered tutoring systems, adaptive learning environments, virtual laboratories, personalized feedback mechanisms, and gamification strategies can collectively transform physics pedagogy. This integration encourages increased student engagement, better conceptual understanding, critical thinking, problem-solving skills, and more positive emotional involvement, thereby creating a more effective and sustainable learning journey. It is concluded that the holistic framework integrating Deep Learning with this pedagogical philosophy offers a promising path to address long-standing challenges in physics education.