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Crowdsourcing-Based Learning: the Effective Smart Pedagogy for STEM Education

机译:基于众包的学习:STEM教育的有效智能教学法

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Multiple innovative teaching and learning strategies, that strongly support the advanced technology-based education, emerged in recent years. However, there are no published data analytics and validated outcomes regarding how good those innovative teaching approaches support the main concepts of Smart Pedagogy. This paper presents the outcomes of the ongoing research, design and development project at the InterLabs Research Institute, Bradley University (Peoria, IL, USA) aimed to identify, analyze, test, implement and recommend various components of Smart Pedagogy for STEM education. Those components may include but are not limited to 1) learning- by-doing, active learning or experiential learning, 2) games-based learning and gamification of learning, 3) collaborative learning, 4) analytics-based learning, 5) flipped classroom, 6) crowdsourcing-based learning, 7) badging-based learning, and 8) productive failure-based learning. The obtained research outcomes about application of crowdsourcing-based learning in several STEM undergraduate and graduate courses are presented in this paper. Project outcomes undoubtedly demonstrate students' keen interest in active use of crowdsourcing-based learning in Sciences courses, in particular, and in highly technological Smart Pedagogy in STEM education, in general.
机译:近年来,出现了多种创新的教学策略,这些策略强有力地支持了以先进技术为基础的教育。但是,关于这些创新的教学方法对智能教学法主要概念的支持程度,目前还没有公开的数据分析和经过验证的结果。本文介绍了布拉德利大学InterLabs研究所(美国伊利诺伊州皮奥里亚)正在进行的研究,设计和开发项目的成果,该项目旨在识别,分析,测试,实施和推荐用于STEM教育的智能教学法的各个组成部分。这些组成部分可能包括但不限于1)边做边学,主动学习或体验学习,2)基于游戏的学习和学习游戏化,3)协作学习,4)基于分析的学习,5)课堂翻转,6)基于众包的学习,7)基于徽章的学习和8)基于生产性失败的学习。本文介绍了基于众包学习在若干STEM本科和研究生课程中应用的研究成果。毫无疑问,项目成果表明了学生对在科学课程中积极使用基于众包学习的兴趣,尤其是对STEM教育中的高科技智能教育学的浓厚兴趣。

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