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A SOPHOMORE LEVEL DATA ANALYSIS COURSE BASED ON BEST PRACTICES FROM THE ENGINEERING EDUCATION LITERATURE

机译:基于工程教育文学的最佳实践的二年级数据分析课程

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As educators are well aware, the customary educational setting in which students develop problem solving skills is one where the numerical values presented are specific and absolute. The deterministic nature of the end-of-chapter type problems is imbedded in their minds well before students even matriculate. However, as practicing engineers, they will confront the variation associated with measured data in the real world. Ideally, it is beneficial to prompt students to attend to the concept of variation early in their undergraduate studies. This paper describes the instructional structure and design of a large sophomore level data analysis and statistics class based on best educational practices. It is delivered to chemical, biological and environmental engineers directly following the material and energy balance courses. The goal of the course is to have students recognize that variation is inevitable, and teach them skills to quantify the variation and make engineering decisions which account for it while still utilizing model based problem solving skills. The instructional design is based on constructivist and social constructivist models of learning. A constructivist perspective views learning as individually constructed based on the learner's prior knowledge, interpretations, and experience with the world, and views cognitive conflict as a stimulus for learning. In addition, a social constructivist perspective views the social interactions and cultural context in which learning occurs as critical. Based on these perspectives, it is believed that learning is facilitated when students (1) are engaged in solving real-world problems, (2) use existing knowledge as a foundation for new knowledge, (3) are immersed in a community centered classroom culture, and (4) are prompted to use metacognative skills and strategies. The course architecture is designed to match the teaching model of Kolb, and encourage the development of intellectual growth as modeled by Perry, in which students' view of knowledge ascends from dualism, to multiplicity of views, and then to contextual relativism. While this paper is presented in a course specific context, it is believed these principles are useful to instructional design, in general.
机译:随着教育者都很清楚,学生发展解决技能的习惯性教育环境是所提供的数值是特定的和绝对的。在学生甚至预科术之前,章节结束类型问题的确定性性质很好地嵌入。然而,作为练习工程师,他们将面临与现实世界中测量数据相关的变化。理想情况下,促使学生在本科学习早期出席学生参加变异概念是有益的。本文介绍了基于最佳教育实践的大二二手级数据分析和统计学课程的教学结构和设计。它是直接追随材料和能量平衡课程的化学,生物和环境工程师。该课程的目标是让学生认识到变化是不可避免的,并教导他们的技能来量化变化并制定工程决策,同时仍然利用基于模型的问题解决技巧。教学设计基于建构主义和社会建构主义学习模型。基于学习者的先验知识,解释和与世界经验的单独构建的建构主义的透视观点,并认为认知冲突作为学习的刺激。此外,社会建构主义的观点意见了社会互动和文化背景,其中学习发生了至关重要。基于这些观点,据信,当学生(1)从事解决现实世界问题时,学习是促进的,(2)使用现有知识作为新知识的基础,(3)沉浸在社区为中心的课堂文化中(4)提示使用代理技能和策略。该课程架构旨在符合KOLB的教学模式,并鼓励佩里建模的知识产病发展的发展,其中学生对知识的看法从二元主义上升,以多种观点,然后对情形相对主义提升。虽然本文在课程具体上下文中呈现,但据信这些原则对于教学设计有用,一般来说。

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