Learning Motivation and Computational Thinking Ability of Elementary School Students in Learning Science - Riska Putri Taupik; Yanti Fitria (Postgraduate, University of Mataram, 2023)
This research is motivated by the low learning outcomes of students in elementary schools, especially in science learning content. This study aims to: (1) analyze learning motivation and students' computational thinking skills in science learning; (2) knowing the relationship between learning motivation and computational thinking ability. This research method is a mixed method, namely research methods that communicate or combine quantitative methods and qualitative methods to be used together in a research activity. This research was conducted in Cluster II Koto Salak, which consists of five elementary schools and one Islamic elementary school. The number of students in grade V at SDa is 25 students for class A, and 26 students for class B, SD B has 26 students, SD C has 15 students, SD D has 10 students, and MI A has 21 students. So that the total number of research subjects is 123 students, plus 5 school principals, and 6 class teachers. Data collection techniques used are questionnaires, observation, structured interviews, and documentation. The qualitative data analysis technique in this study is to use interactive data analysis, namely (1) data reduction; (2) Data presentation; (3) Drawing conclusions. Quantitative data analysis is the Pearson correlation test using SPSS. The results showed that the learning motivation and computational thinking abilities of students in cluster II Koto Salak were still low, on average, still below 30%. Pearson correlation test results Sig 0.00 <0.05 so that H1 is accepted, meaning that learning motivation and computational thinking ability have a correlation. The form of the correlation is positive with a value of 0.99 which is at the perfect relationship level.