Impact of Deep Learning–Based PhET Instruction on Problem-Solving Skills: The Role of Learning Motivation - Hidayatullah, Zul; Ariandani, Nunung; Qusyairi, Muhammad; Marzuki, Muhammad (Department of Physics Education, Universitas Mataram, 2025)
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.