首页> 外文会议>ASME Fluids Engineering Division summer meeting >TOUCHPAD IN EDUCATION: DYNAMIC LEARNING FRAMEWORK ASSESSMENT AND CONTENT DEVELOPMENT FOR THE UNDERGRADUATE FLUID MECHANICS
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TOUCHPAD IN EDUCATION: DYNAMIC LEARNING FRAMEWORK ASSESSMENT AND CONTENT DEVELOPMENT FOR THE UNDERGRADUATE FLUID MECHANICS

机译:图卡帕德教育学:本科流体力学的动态学习框架评估和内容发展

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摘要

This paper presents a dynamic learning framework (DLF) based on dynamic course contents and assessment methods using latest web-based technologies with keeping in mind the recent advancement in touchpad computing devices (such as IPAD and Android based tablets). In the DLF framework, the effectiveness is assessed via evaluating the learning outcomes of increasing the learnability of high level concepts in the Bloom's Taxonomy of cognitive learning. It proposes to address the challenges is creating a fluid mechanics module that incorporates all levels of the Bloom's cognitive taxonomy. This is achieved via integration of mathematical, conceptual and visual contents. The lower level concepts (i.e., Remembering, Understanding, and Applying) are computerized and tested using Computer Adaptive Testing (CAT) algorithm. Our targeted audiences are from a predominantly Hispanic cultural setting and in undergraduate mechanical engineering courses. To capitalize on unique cultural setting and linguistic needs, the assessment is prepared in bi-lingual (Spanish and English) with localized problems. A pre-assessment of students' learning styles was performed to assess their learning preference and the presentation was tuned to average audiences. It was observed that about 10% of the students used bi-lingual instructions in the exam which was conducted as an extra-credit option to paper based exam in order to assess the DLF framework. Students were also asked to contribute questions to generate a question database with localized problems.
机译:本文介绍了一种动态学习框架(DLF),该框架基于动态课程内容和评估方法,使用最新的基于Web的技术,同时牢记触摸板计算设备(例如IPAD和基于Android的平板电脑)的最新发展。在DLF框架中,通过评估在Bloom的认知学习分类法中提高高级概念的可学习性的学习结果来评估有效性。它提出了解决挑战的方法,即创建一个融合了Bloom认知分类学各个层次的流体力学模块。这是通过整合数学,概念和视觉内容来实现的。使用计算机自适应测试(CAT)算法对较低级别的概念(即,记住,理解和应用)进行计算机化和测试。我们的目标受众是来自主要是西班牙裔的文化背景和大学机械工程课程的学生。为了充分利用独特的文化背景和语言需求,评估以双语(西班牙语和英语)进行,且存在局部问题。进行了学生学习风格的预评估,以评估他们的学习偏好,并且将演示文稿调整为普通受众。据观察,大约10%的学生在考试中使用了双语指导,这是对纸笔考试的额外加分,以评估DLF框架。还要求学生提出问题,以生成具有本地化问题的问题数据库。

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