Assessment of Attitude tetep, antep, mantep in the Teaching: Ki Hadjar Dewantara Perspective

R. Retnaningsih, Samsi Haryanto, Awang Damar, Puspa Eka Ayu Febriana
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Abstract

Ki Hadjar Dewantara's tetep, antep, and mantep attitude assessment is the focus of this research. Descriptive qualitative research is conducted in the form of a literature review that objectively examines the process of developing Ki Hajar Dewantara's attitude evaluation instrument (tetep, antep, mantep). The purpose of the descriptive method is to provide sufficient background information for further analysis and conclusion drawing. Analysis and recording of studies serve as the basis for the data collection. Books and articles are gathered for research at libraries. Data collected from documentation and field data poured in a thorough and detailed report description form the basis of the interactive model analysis. A summary is created at the outset of data gathering, which is when data reduction occurs. The next step is trimming the data and field reports to the essentials, highlighting the most relevant information, and looking for recurring themes or patterns. Data is reduced in complexity by selection, classification, and abstraction. Data visualisation aids researchers in gaining a better understanding of the whole or of specific subsets of their data. Fourth, by categorising the data, researchers are able to draw meaningful conclusions. The study's findings indicated that the sensitivity index value was the most reliable predictor of Ki Hadjar Dewantara's tetep, antep, and mantep attitudes towards his students' academic progress. Contextual learning models that incorporate local expertise can be used to refine evaluations of Ki Hadjar Dewantara's tetep, antep, mantep attitude.
教学中testep, antep, mantep态度的评估:Ki Hadjar dewanara视角
Ki Hadjar dewanara的tetep, antep和mantep态度评估是本研究的重点。描述性定性研究以文献综述的形式进行,客观地考察了Ki Hajar dewanara的态度评估工具(tetep, antep, mantep)的发展过程。描述性方法的目的是为进一步分析和得出结论提供足够的背景信息。研究的分析和记录是数据收集的基础。图书馆收集书籍和文章作研究之用。从文档和现场数据中收集的数据,在一个全面而详细的报告描述中形成了交互式模型分析的基础。在数据收集开始时创建摘要,这是数据缩减发生的时候。下一步是将数据和领域报告精简到最重要的部分,突出显示最相关的信息,并寻找重复出现的主题或模式。通过选择、分类和抽象,可以降低数据的复杂性。数据可视化有助于研究人员更好地理解其数据的整体或特定子集。第四,通过对数据进行分类,研究人员能够得出有意义的结论。研究结果表明,敏感性指数值是Ki Hadjar dewanara对学生学业进步的tetep、antep和mantep态度的最可靠的预测指标。结合当地专业知识的情境学习模型可以用来完善对Ki Hadjar dewanara的testep, antep, mantep态度的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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