以问题为基础,以社会科学问题为主题的电子模块提高大学生的议论文能力

A. F. R. Wati, M. Sari, Suhadi Suhadi
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引用次数: 0

摘要

电子模块被认为是一种有效的学习媒体,可以创造一个互动的学习环境。然而,以问题为基础的学习模式为基础,将社会科学问题整合到学生的论证技能中,对电子模块的开发进行考察的研究却很少。本研究旨在透过以社会科学议题为主题,以问题为基础的电子模组,培养学生的议论文技巧。研究者采用一组前测后测设计的预实验方法。该电子模块于2022年11月至12月在玛琅州立大学生物教育学院的36名学生中实施,为第二年。研究样本采用非随机抽样。研究样本采用非随机抽样方法选取。研究数据采用开放式问题收集,以评估学生的辩论技巧,并根据辩论技巧分类。采用归一化增益法(N-Gain)对测试前和测试后的分数进行分析。N-Gain计算结果为0.46分,即学生在实施基于问题的社会科学问题主题电子模块后,其论证能力发展到中等水平。以社会科学问题为主题的问题为基础的电子模块可以训练学生识别问题,分析数据和证据,以提供复杂论证包装的解决问题的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Undergraduate Students’ Argumentation Skills Through Problem-Based E-Module with Socio-Scientific Issues Topics
E-module is considered as an effective learning media to create an interactive learning environment. In spite of that it is rare to find the research that examines the development of e-module based on Problem-Based Learning model that integrates Socio-Scientific Issues on argumentation skills to students. This research aims to develop students’ argumentation skills through problem-based e-module with socio-scientific issues topics. The researcher used pre-experimental method with one-group-pretest-posttest design. The e-module was implemented to 36 students of Biology Education, State University of Malang, for the 2nd-year from November to December 2022. The research sample was selected by non-random sampling. The research samples were selected by non-random sampling. The research data were collected by opened-ended questions to assess the students’ argumentation skills and categorized based on the argumentation skills rubric. The data were analyzed based on the increase of pre-test and post-test scores by using Normalized Gain (N-Gain).  The result of the N-Gain calculation showed a score of 0.46 which means the students’ argumentation skills developed in the moderate category after the implementation of using problem-based e-module with socio-scientific issues topics. The problem-based e-module with socio-scientific issues topics can train students to identify the problems, analyze data and evidence to give problem-solving solutions packaged of complex argumentation.
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