基于发现学习的桑巴民俗丰富的模块开发——生物亚物质分类研究

Siti Fatimah, Wolly Candramila, Andi Besse Tenriawaru
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引用次数: 0

摘要

本研究旨在以发现学习方法结合民俗学,开发《生物亚物质生物分类》教材模块,并确定学生对教材的反应。本研究采用Thiagarajan四维模型开发方法的前三个阶段:定义、设计、开发和传播。研究工具为验证表和问卷。本研究的样本为高中X班15人。五名产品验证者包括来自丹戎普拉大学数学与自然科学学院生物学研究项目的两名讲师,来自Pontianak市两所高中的三名生物教师,以及来自Sambas Regency的一名生物教师。模块验证表包括内容、语言、演示、图形4个方面,共20个指标。学生反应问卷由认知、情感和创造性三个方面组成,共26条陈述。问卷的验证者是来自丹戎普拉大学生物教育研究计划的2位讲师。开发一个基于发现学习生物子材料分类的模块是可行的,因为它的效度值为CVR = 1,即该模块在内容上是有效的。宣布可行的模块在有限的基础上进行测试,并在非常强的类别中获得81.9%的平均结果。在更大的班级中测试模块需要进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery-learning-based Module Development Enriched with Sambas Folklore on the Sub Material Classification of Living Things
This study aims to develop a module as teaching materials on the Sub Material Living Things Classification of living things sub-material with a discovery learning approach combined with folklore and determine students' responses to these teaching materials. This study uses the first three stages of the Thiagarajan 4D model development method: define, design, develop, and disseminate. The research instruments used were validation sheets and questionnaires. The sample in this study was class X high school students consisting of 15 people. The five product validators consisted of two lecturers from the Biology Study Program, Faculty of Mathematics and Natural Sciences, Tanjungpura University, three biology teachers from 2 high schools in Pontianak City, and one from Sambas Regency. The module validation sheet consists of 4 aspects, namely content, language, presentation, and graphics consisting of 20 indicators. The student response questionnaire consists of three aspects, namely cognitive, affective, and conative aspects, comprising 26 statements. The response questionnaire validators consisted of 2 lecturers from the Biology Education Study Program, FKIP, University of Tanjungpura. Developing a module based on discovery learning sub-material classification of living things is declared feasible because it obtains a validity value of CVR = 1, meaning that the module is valid in content. Modules declared feasible are tested on a limited basis and obtain an average result of 81.9% in the very strong category. Further research is needed to test modules in larger classes.
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