Problem solving ability with mathematical modeling strategy in term of mathematics self-efficacy on Generative Learning Model
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Abstract
Problem Solving Ability (PSA) is the ability of students to determine how to solve a mathematical problems that have not yet been known how to solve it. Abstract mathematical concepts become obstacles in solving mathematical problems. Mathematical Modeling Strategy (MMS) is one ways that can be used to improve students' problem solving ability. In addition, one learning model that can improve students' problem solving ability is Generative Learning Model (GLM). The purpose of this study is to (1) find out whether the results of the PSA test achieve mastery learning, (2) find out whether the results of the PSA test have improved, (3) find out the relationship between the level of Mathematics Self-Efficacy (MSE) on students' PSA, and (4) describe the PSA in terms of students' MSE. The research method that used is mixed methods with quantitative methods using Pre-Experimental Design that modela is One-Group Pretest-Posttest Design. The research sample was taken with a multistage cluster sampling technique and research subjects were selected with a purposive sampling technique. The results showed that; (1) students' PSA achieve a mastery learning, (2) students' PSA have increased, (3) there is a positive influence between the level of MSE on students' PSA, (4) students with high levels of MSE tend to have better PSA than students with moderate and low levels of MSE.
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