Impact of Deep Learning–Based PhET Instruction on Problem-Solving Skills: The Role of Learning Motivation
DOI:
10.29303/jpft.v11i2.10233Published:
2025-12-08Issue:
Vol. 11 No. 2 (2025): July - December (In Press)Keywords:
deep learning, PhET, problem-solving, learning motivationArticles
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Abstract
This study aims to analyze the impact of the implementation of PhET simulation-assisted deep learning on students' problem-solving skills by considering their level of learning motivation. The research method used a quasi-experimental design with a 2x2 factorial model. The research subjects involved two classes: an experimental class that received PhET simulation-assisted deep learning treatment and a control class that received conventional learning. The research instruments included a problem-solving skills test in the form of essay questions and a validated learning motivation questionnaire. Data analysis was conducted using a two-way ANOVA test to examine the effect of learning methods, motivation levels, and their interaction on problem-solving skills. The results showed that PhET simulation-assisted deep learning significantly improved problem-solving skills compared to conventional learning. Students with high learning motivation achieved better problem-solving scores than students with low motivation, both in the experimental and control groups. In addition, there was a significant interaction between learning methods and learning motivation, where students with high motivation who participated in deep learning showed the highest improvement in problem-solving skills. These findings confirm that PhET simulation-assisted deep learning is effective for developing critical thinking and problem-solving skills, especially in students with high learning motivation. The implications of this research encourage educators to integrate in-depth learning and technology-based interactive media in the science learning process, as well as pay attention to strategies for increasing learning motivation as an important factor in achieving optimal learning outcomes.
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Author Biographies
Zul Hidayatullah, Universitas Hamzanwadi
Science Education Study Program
Nunung Ariandani, Universitas Hamzanwadi
Science Education Study Program
Muhammad Qusyairi, Universitas Hamzanwadi
Computer Engineering
Muhammad Marzuki
Science Education
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Copyright (c) 2025 Zul Hidayatullah, Nunung Ariandani, Muhammad Qusyairi, Muhammad Marzuki
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