Implementasi Strategi Pembelajaran Think Pair Share (Tps) Menggunakan Aplikasi Nearpod Terhadap Kemampuan Pemecahan Masalah Dan Minat Belajar Siswa

Problem-solving ability is one of the important competencies that students must have in order to face challenges in everyday life, besides that high interest in learning is also a determining factor for success in the learning process. This study aims: (1) to find out whether there are differences in the initial ability of experimental and control class students, (2) to find out whether students' problem-solving abilities after using the Think Pair Share learning model using the Nearpod application are better than classes given conventional learning, (3) to find out whether students' learning interest after using the Think Pair Share learning model using the Nearpod application is better than the class that given conventional learning. The method in this study is with quantitative methods that are experimental. The population of this study is all grade X students of SMK Insan Cendekia Kragilan in the odd semester of the 2023/2024 academic year. The sample of this study was 26 X A students as an experimental class and 26 X B students as a control class, so the sample in this study was 52 students. From the calculation results, the Sample t-Test Pretest is obtained, namely the Sig. (2-tailed) Value of 0.163 < 0.05 so that H0 is accepted and H1 is rejected. The posttest t-Test sample is a Sig. (2-tailed) value of 0.000 < 0.05 so that H0 is rejected and H1 is accepted. So it was concluded that: (1) There was no difference in the initial ability of experimental class and control class students (2) The ability to solve problems in Trigonometric material using the Think Pair Share (TPS) Learning Model using the Nearpod application was better than the ability to solve problems using conventional learning models. (2) Student learning interest in Trigonometry material using the Think Pair Share (TPS) learning model using the Nearpod application is better than student learning interest using conventional learning models