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Rudi Purwanto
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INDONESIA
Jurnal Inovasi Fisika dan Edukasi
Published by Institut of Educational, Research, and Community Service
ISSN : -     EISSN : 31092608     DOI : -
Core Subject : Science, Education,
Education Physics
Jurnal Inovasi Fisika dan Edukasi (Journal of Physics Innovation and Education) is a scientific journal published by the Institute of Educational, Research, and Community Service (inercys). This journal aims to serve as an academic platform for the publication of research articles, literature reviews, theoretical studies, and innovative developments in the fields of physics and physics education. Its primary focus includes the advancement of concepts, approaches, and technologies in physics learning, as well as other scientific contributions intended to improve the quality of physics education at various educational levels. The journal welcomes submissions from researchers, lecturers, teachers, students, and education practitioners who are interested in innovations in physics and physics education. Jurnal Inovasi Fisika dan Edukasi is published twice a year, in June and December, and has been in publication since 2025. All submitted articles undergo a rigorous peer-review process to ensure their scientific quality and academic contribution.
Arjuna Subject : Fisika dan Astronomi - Fisika dan Astronomi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2025): December" : 5 Documents clear
EXPLORATION OF PHYSICS LEARNING USING ADAPTIVE ARTIFICIAL INTELLIGENCE-BASED VIRTUAL UNIVERSE SIMULATION MODELS FOR SCHOOL STUDENTS Herman Tino; Baik Anita Febriana
Jurnal Inovasi Fisika dan Edukasi Vol. 1 No. 2 (2025): December
Publisher : INERCYS: Institute of Educational, Research, and Community Service

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Abstract

The exploration of physics learning using an adaptive artificial intelligence–based virtual universe simulation model represents an important aspect of physics education due to the limitations of conventional instruction in visualizing abstract and multiscale physical phenomena. This challenge highlights the need for experimental research that empirically examines the effectiveness of adaptive virtual simulation models in improving students’ conceptual understanding of physics. This study aims to analyze the impact of implementing an adaptive artificial intelligence–based virtual universe simulation on students’ conceptual understanding and learning engagement in school physics. The research employed an experimental method with a quasi-experimental design involving an experimental group and a control group. Data were collected through conceptual understanding tests, learning activity observation sheets, and student engagement questionnaires. The collected data were analyzed using descriptive statistics and independent samples t-tests. The results indicate that students who learned through adaptive virtual simulations achieved a statistically significant improvement in conceptual understanding scores (p < 0.05), with an average increase of more than 20% compared to the control group. These findings suggest that adaptive virtual universe simulation–based physics learning is effective in enhancing the quality of physics instruction. This study contributes to the development of intelligent technology-based physics learning models and provides empirical evidence supporting the integration of artificial intelligence in science education.
PHYSICS LEARNING BASED ON CONCEPTUAL ERROR PREDICTION USING LARGE LANGUAGE MODEL AS A COGNITIVE ASSISTANT FOR SCHOOL STUDENTS Saepudin
Jurnal Inovasi Fisika dan Edukasi Vol. 1 No. 2 (2025): December
Publisher : INERCYS: Institute of Educational, Research, and Community Service

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Abstract

Physics learning based on the prediction of conceptual errors is a critical aspect of physics education, as persistent student misconceptions often hinder the development of deep conceptual understanding. This challenge highlights the need for adaptive learning approaches capable of identifying and anticipating students’ conceptual errors at an early stage. This study aims to examine the effectiveness of implementing a Large Language Model as a cognitive assistant in predicting conceptual errors and enhancing students’ conceptual understanding and learning engagement. The research employed an experimental method with a quasi-experimental design. Data were collected through conceptual understanding tests, learning engagement observation sheets, and instructional documentation, and were analyzed using descriptive statistics and inferential analysis through an independent samples t-test. The results indicate that the experimental group achieved a higher mean post-test score in conceptual understanding than the control group (M = 81.9; SD = 6.7 vs. M = 68.1; SD = 7.4), with a statistically significant difference (t(58) = 6.05, p < 0.001) and a strong effect size (Cohen’s d = 0.93). In addition, the level of conceptual errors among students in the experimental group decreased significantly, while learning engagement was classified as high (M = 4.18) compared to the control group (M = 3.34). These findings demonstrate that a Large Language Model can function effectively as a cognitive assistant by predicting patterns of conceptual errors and providing adaptive scaffolding in real time. This study makes a significant contribution to the development of artificial intelligence–based physics learning models, extends the application of constructivist theory and cognitive load theory within intelligent digital learning environments, and enriches the international literature on the use of Large Language Models in science education. Furthermore, the findings are expected to serve as a reference for the development of adaptive physics learning and for future research on integrating Large Language Models as pedagogical agents at the school level.
A PRELIMINARY STUDY OF PHYSICS LEARNING THROUGH THE INTEGRATION OF MULTIVERSE NARRATIVE AND HYPOTHETICAL PHYSICS MODELING IN ENHANCING STUDENTS' SCIENTIFIC IMAGINATION Pathurrahman; Rudi Purwanto
Jurnal Inovasi Fisika dan Edukasi Vol. 1 No. 2 (2025): December
Publisher : INERCYS: Institute of Educational, Research, and Community Service

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Abstract

Physics learning through the integration of multiverse narrative and hypothetical physics modeling constitutes an important aspect of physics education because conventional instructional approaches often limit students’ scientific imagination and their ability to explore abstract, speculative, and frontier concepts in modern physics. This limitation highlights the need for further research focusing on instructional strategies that explicitly promote imaginative reasoning while preserving scientific rigor. This preliminary study aims to examine the effectiveness of integrating multiverse narratives with hypothetical physics modeling in enhancing students’ scientific imagination and learning engagement in physics. The study employed an experimental method with a quasi-experimental design involving an experimental group and a control group. Data were collected using scientific imagination assessment tests, learning engagement observation sheets, and instructional documentation, and were analyzed through descriptive statistics and inferential analysis using an independent samples t-test. The results reveal that students in the experimental group demonstrated significantly higher scientific imagination scores (M = 84.2; SD = 6.5) compared to the control group (M = 71.6; SD = 7.3), with a statistically significant difference (t(58) = 5.72, p < 0.001) and a strong effect size (Cohen’s d = 0.88). In addition, learning engagement in the experimental group was categorized as high (M = 4.21) relative to the control group (M = 3.36), indicating more active participation in exploratory discussion, conceptual speculation, and reflective reasoning. These findings illustrate that the integration of multiverse narratives with hypothetical physics modeling effectively fosters scientific imagination and cognitive engagement by providing a structured space for speculative yet theory-informed thinking. This study contributes significantly to the advancement of physics education at both national and international levels by proposing an innovative pedagogical framework that bridges narrative-based learning, theoretical modeling, and imaginative science education. Furthermore, the findings are expected to serve as a reference for future research on creative physics instruction, speculative science learning, and interdisciplinary approaches that support the development of higher-order thinking skills in 21st-century physics education.
QUANTUM STORYTELLING ARTIFICIAL INTELLIGENCE (AI) BASED PHYSICS LEARNING TO ENHANCE STUDENTS' CONCEPTUAL INTUITION ON NON-CLASSICAL PHENOMENA Zohri Ratna
Jurnal Inovasi Fisika dan Edukasi Vol. 1 No. 2 (2025): December
Publisher : INERCYS: Institute of Educational, Research, and Community Service

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Abstract

Quantum storytelling artificial intelligence–based physics learning is an important aspect of physics education because conventional instructional approaches often fail to support students in developing strong conceptual intuition toward non-classical phenomena that are abstract, probabilistic, and counterintuitive, such as superposition, entanglement, and the quantum uncertainty principle. This limitation highlights the need for further research focusing on innovative instructional strategies that integrate artificial intelligence–driven narrative approaches to bridge students’ conceptual understanding of quantum phenomena. This study aims to examine the effectiveness of quantum storytelling–based physics learning supported by artificial intelligence in enhancing students’ conceptual intuition of non-classical phenomena. The research employed an experimental method with a quasi-experimental design involving an experimental group and a control group. Data were collected through quantum conceptual intuition tests, learning engagement observation sheets, and instructional documentation. The collected data were analyzed using descriptive statistics and inferential analysis through an independent samples t-test. The results indicate that students in the experimental group achieved significantly higher conceptual intuition scores (M = 83.7; SD = 6.4) than those in the control group (M = 70.9; SD = 7.6), with a statistically significant difference (t(58) = 5.61, p < 0.001) and a strong effect size (Cohen’s d = 0.86). These findings demonstrate that the integration of quantum storytelling supported by artificial intelligence effectively strengthens students’ conceptual intuition through narrative, visual, and reflective representations aligned with quantum physics principles. This study provides a significant contribution to the advancement of physics education at both national and international levels by proposing an innovative artificial intelligence–based narrative learning model for non-classical physics instruction. Furthermore, the findings are expected to serve as a reference for future research in quantum physics education, artificial intelligence–supported science learning, and studies on conceptual intuition in abstract physics domains.
THE EFFECT OF PHYSICS LEARNING USING FUTURE SELF DIGITAL AVATARS ON STUDENTS' MOTIVATION AND LEARNING RESILIENCE Laelatul Munawaraha; Muhammad Aminuddin
Jurnal Inovasi Fisika dan Edukasi Vol. 1 No. 2 (2025): December
Publisher : INERCYS: Institute of Educational, Research, and Community Service

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Abstract

Physics learning using future self digital avatars constitutes an important aspect of physics education because traditional instructional approaches often fail to sustain students’ motivation and resilience in facing challenging concepts and problem-solving tasks. This limitation highlights the need for further research focusing on innovative digital strategies that leverage self-representative avatars to enhance learning engagement and perseverance. This study aims to examine the effect of physics learning mediated by future self digital avatars on students’ motivation and learning resilience. The research employed a quantitative approach with an experimental method and a quasi-experimental design involving an experimental group and a control group. Data were collected using standardized motivation questionnaires, learning resilience scales, and instructional documentation. The collected data were analyzed using descriptive statistics and inferential analysis through independent samples t-tests. The results indicate that students who participated in physics learning using future self digital avatars demonstrated significantly higher motivation scores (M = 82.4; SD = 5.9) and learning resilience (M = 79.6; SD = 6.3) compared to the control group (motivation: M = 70.2; SD = 7.1; resilience: M = 68.5; SD = 7.8), with statistically significant differences (motivation: t(58) = 6.12, p < 0.001; resilience: t(58) = 5.87, p < 0.001) and strong effect sizes (Cohen’s d = 0.90 and 0.88, respectively). These findings suggest that integrating future self digital avatars into physics learning effectively strengthens students’ intrinsic motivation and their capacity to persist through challenging learning tasks. The study provides significant contributions to the advancement of physics education at both national and international levels by offering an innovative pedagogical model that combines digital self-representation, motivational scaffolding, and resilience-building strategies. Furthermore, the findings are expected to serve as a reference for future research on digital avatar–mediated learning, student motivation, and resilience in science education, as well as to encourage further development of immersive and personalized instructional approaches.

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