La Sinara, Wa Irda
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Analisis Tingkat Motivasi Belajar dan Self-Efficacy Siswa Melalui Model RQANI (Reading, Questioning, Answering, Elaboration, and Integration) Kelas XI SMA Negeri 6 Kota Ternate La Sinara, Wa Irda; Amin, Astuti Muh; Az Umangap, Wirda
Al-Nafis: Jurnal Biologi dan Pendidikan Biologi Vol 4, No 1 (2024): Al-Nafis: Jurnal Biologi dan Pendidikan Biologi
Publisher : IAIN Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46339/al-nafis.v4i1.1296

Abstract

Self-efficacy is very important in increasing students' learning motivation. From the preliminary study conducted by researchers, information was obtained that there are several problems in biology learning which influence learning motivation and self-efficacy. The research objectives were (1) to analyze the level of learning motivation of class XI students at SMA Negeri 6 Ternate City after learning using the RQANI (Reading, Questioning, Answering, Elaboration, and Integration) model; (2) to analyze the level of self-efficacy of class XI students at SMA Negeri 6 Ternate City after learning using the RQANI (Reading, Questioning, Answering, Elaboration, and Integration) model. This research uses a quantitative type of research. The research was conducted in the odd semester of the 2023/2024 academic year. The instruments used to collect data are learning motivation and self-efficacy instruments. Both instruments have previously been declared valid and reliable. The research results include (1) the average student learning motivation for the attention aspect is 78.48; relevance 77.95; confidence 69.93;, satisfaction 79.83; (2) students who obtained the very high self-efficacy category were 50.00%; high self-efficacy amounted to 43.75%; low self-efficacy was 6.25% and very low self-efficacy was 0%. The level of self-efficacy of class It is hoped that this research can contribute to further research in order to increase students' learning motivation and self-efficacy through the application of innovative learning models.