Improving students' physics representation competence with an Android-based representation training model

Jane Koswojo, Sentot Kusairi, Sutopo Sutopo, Edi Supriana

Abstract


epresentational competence helps students understand and communicate complex physics concepts. High representational competence can simplify the physics learning and problem-solving process. Unfortunately, research shows a lack of students' representational competence and significant challenges for teachers in improving it. This research aims to develop an Android-based representation training model to teach linear motion kinematics. The model development followed the research and development (R&D) method with ADDIE design. Data were collected through questionnaires, interviews, and observations of 127 undergraduate students. This research successfully built an Android-based representational competence training model with feedback and scaffolding. This representational competence training can help students understand physics concepts through various representations. Findings from the experimental test show that the representation training model can be effectively used to improve students' representation competence. The N-Gain score of experimental class I, 0.35, and experimental class II, 0.61, with moderate criteria, illustrates this improvement. In addition, Kruskal Wallis analysis shows that the use of this model can improve representation competence if used in the learning process. More in-depth research is needed to determine the impact of the representational training model on student learning processes and outcomes.

Keywords


representation training model; kinematics; linear motion; android; training model

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DOI: https://doi.org/10.11591/edulearn.v20i3.24547

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