Contribution: This article aims to explore learner variables that predict the effectiveness '/> Effects of Applying the Havruta Method in Class: A Study on Targeting Learner Variables in the “General Physics and Experiments 2” Class
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Effects of Applying the Havruta Method in Class: A Study on Targeting Learner Variables in the “General Physics and Experiments 2” Class

机译:应用HAVRUTA方法在课堂上的影响:瞄准学习者变量在“普通物理与实验2”课上的研究

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Contribution: This article aims to explore learner variables that predict the effectiveness of university class using the Havruta method. Background: This article was conducted on 105 learners enrolled in the “General Physics and Experiments 2” class at K University in South Korea. Research Questions: Independent and dependent variables were selected from previous studies to predict the effectiveness of the Havruta method in a university class. Methodology: Descriptive statistics, such as frequency and averages, were used to analyze the general characteristics of the subjects, and Pearson’s $r$ was used to check for correlation among all variables. Variables that predicted learning satisfaction and flow were evaluated through the multiple regression analysis. Findings: The results indicate that the independent variable, “openness to learning opportunities” significantly explained learning satisfaction by 16.1%. Together with “initiative and independence in learning,” they were significant in explaining the learning flow by 32.2%. These findings have practical application in universities in integrating the Havruta method to their classes.
机译:<斜体XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”>贡献: 本文旨在探索使用HAVRUTA方法预测大学课程的有效性的学习者变量。背景: 本文于105名学习者进行,参加了韩国K大学的“通用物理和实验2”课程。研究问题: 从以前的研究中选择独立和依赖变量,以预测HAVRUTA方法在大学课程中的有效性。<斜体XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”>方法: 描述性统计数据,例如频率和平均值,用于分析受试者的一般特征和Pearson的<内联公式XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”> $ r $ 用于检查所有变量之间的相关性。通过多元回归分析评估预测学习满足和流量的变量。 condings: 结果表明,独立变量,“开放的学习机会”明显解释了16.1%的学习满意度。与“学习的主动和独立性”在一起,它们在解释学习流量32.2%时显着。这些调查结果在将HAvruta方法整合到课程中具有实际应用。

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