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Teaching Sensor Technology and Crowdsourcing with Reusable Learning Objects

机译:用可重复使用的学习对象教学传感器技术和众群

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摘要

Reusable learning objects (RLOs) are self-contained digital modules commonly utilized in e-learning. The purpose of this study is to develop RLOs for teaching soil color determination using a color sensor application: soil color, Soil Scanner application, global positioning system (GPS) location, color conversion, and crowdsourcing. Each RLO is a self-contained learning unit with specific learning goals, educational materials, quiz, and assessment. Navigation of each object is controlled by the participant via tabs to allow the user to control the pace of the RLO. The quality of each RLO is assessed by a learning object review instrument (LORI) framework rubric. Online quizzes at the end of each RLO are used to examine the learning outcomes. This study also used the web-based survey tool Qualtrics before and after the laboratory-based activity to systematically measure various constructs including familiarity with sensors, crowdsourcing, and perception of the sensor-based method of soil testing. Reusable learning objects were effective teaching tools as demonstrated by excellent scores (A) received by the students for all RLO quizzes. Each RLO scored well for each category of the LORI model framework assessment. Additional comments suggest that students were receptive to the RLOs as a learning tool. Students positively described their perception of the sensor-based method of soil testing compared with the traditional method, a Munsell color chart.
机译:可重用的学习对象(RLO)是在电子学习中通常使用的自包含数字模块。本研究的目的是使用颜色传感器应用开发RLO用于教学土壤颜色测定:土壤颜色,土壤扫描仪应用,全球定位系统(GPS)位置,颜色转换和众包。每个RLO都是一个独立的学习单元,具有特定的学习目标,教育材料,测验和评估。每个对象的导航由参与者通过选项卡控制,以允许用户控制RLO的速度。每个RLO的质量由学习对象审查仪器(LORI)框架标题进行评估。每个RLO结束时在线测验用于检查学习结果。本研究还使用了基于实验室的活动前后的基于网络的测量工具音质,以系统地测量各种构造,包括熟悉传感器,众包和对土壤测试的传感器的方法的感知。可重复使用的学习对象是有效的教学工具,通过学生收到的所有RLO测验所接收的优秀分数(a)所证明。每个RLO都适用于每种类别的Lori模型框架评估。额外评论表明,学生作为学习工具接受RLO。与传统方法,Munsell颜色图表相比,学生们对基于传感器的土壤测试方法的看法。

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  • 来源
    《Natural Sciences Education》 |2018年第1期|共18页
  • 作者单位

    Dep. of Forestry and Environmental Conservation Clemson Univ. Clemson SC 29634;

    Dep. of Forestry and Environmental Conservation Clemson Univ. Clemson SC 29634;

    Dep. of Forestry and Environmental Conservation Clemson Univ. Clemson SC 29634;

    School of Computing Clemson Univ. Clemson SC 29634;

    School of Computing Clemson Univ. Clemson SC 29634;

    Clemson Univ. Office for Institutional Assessment Clemson SC 29634;

    Clemson Univ. Office for Institutional Assessment Clemson SC 29634;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业科学;
  • 关键词

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