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Educating the engineers of 2020: An outcomes-based typology of engineering undergraduates.

机译：教育2020年的工程师：基于工程学的本科生基于结果的类型学。

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摘要

Members of government and industry have called for greater emphasis within U.S. colleges and universities on producing engineers who can enter and advance a more competitive, globally connected workforce. Looking toward this future, engineers will need to exhibit strong analytical skills as in the past, but they also will need to be proficient in a cadre of new abilities to compete. This study examines, in combination, an array of knowledge and skills aligned with the National Academy of Engineering's "engineer of 2020." The study has two major goals. The first is to develop a typology of engineering students based on the learning outcomes associated with the engineer of E2020. The second is to understand the educational experiences that distinguish these groups of students who resemble, more or less, the engineer of 2020. This approach acknowledges that engineering graduates need a complex skill set to succeed in the new global economy; it is the combination of skills associated with the engineer of 2020, not the individual skills in isolation, which will ensure graduates can respond to workforce needs of the future. To date, research on student outcomes has studied learning outcomes independent of one another rather than investigating student learning holistically.;The study uses student data from the Prototype to production: Processes and conditions for preparing the Engineer of 2020 study, sponsored by the National Science Foundation (NSF EEC-0550608). Engineering students from a nationally representative sample of engineering programs in the United States answered a survey that collected information on their pre-college academic preparation and sociodemographic characteristics, their curricular and co-curricular experiences in their engineering programs, and their self-ratings of their engineering-related competencies. Only data on engineering students in their senior year (n=2,422) were utilized in analyses.;Analyses were conducted in multiple phases for each of five engineering disciplines in the data set (biomedical/bioengineering, chemical, civil, electrical, and mechanical engineering). First, cluster analyses produced typologies (or groupings) of engineering seniors (one for each of five engineering disciplines studied and an "all engineering" analysis) based on nine self-reported learning outcomes, including fundamental skills, design skills, contextual awareness, interdisciplinary competence, and professional skills. Second, profiles of pre-college characteristics as well as student experiences in college were developed for each discipline and the five disciplines combined. Using analyses of variance, Chi-square analyses, and multinomial logistic regression, this phase also identified differences in student characteristics and college experiences between clusters of students reporting high proficiencies on the array of outcomes and students in other clusters. This second phase informed the third phase, which produced parsimonious models that used pre-college characteristics and student experience variables to predict cluster membership.;As a whole, the findings demonstrate that analyses that include the full array of E2020 learning outcomes produce meaningful typologies that distinguish between groupings of students in different engineering fields. Findings demonstrate that a subset of students – the engineers of 2020 – report high skills and abilities on the full array of learning outcomes. These are the graduates sought by both the federal government and industry who most closely resemble the engineers of 2020. In addition, distinctive curricular and co-curricular experiences distinguish this E2020 group of students in each engineering discipline from other groupings of students in that same discipline. These findings have valuable implications for practice because they identify an array of discipline-specific, in- and out-of-class learning experiences that appear to promote the development of this multi-dimensional set of outcomes. Overall, however, greater curricular emphases on broad and systems perspectives in the engineering curriculum most consistently set apart the students who report high proficiencies on the E2020 outcomes. The findings also indicate that strategies for improving undergraduate engineering outcomes should be tailored by engineering discipline. The study contributes to both practice and research by developing a technique that can be used to create an outcomes-based typology that can be applied to any set of learning outcomes. Graphical representations of results consolidate large quantities of information into an easily accessible format so that findings can guide both practitioners and policymakers who seek to improve this multi-dimensional set of undergraduate engineering learning outcomes. Future directions for research, including operationalizing organizational contexts influencing E2020 learning outcomes as well as anticipated career trajectories of students across the typology, are also discussed.
机译：政府和行业成员呼吁在美国的大学和学院中更加强调能够聘用和发展更具竞争力的全球联系的劳动力的工程师。展望未来，工程师将需要像过去一样展现出强大的分析能力，但他们也需要精通一系列新的竞争能力。这项研究结合了一系列与国家工程学院的“ 2020年工程师”相一致的知识和技能。该研究有两个主要目标。首先是根据与E2020的工程师相关的学习成果来开发工科学生的类型。第二个是要了解将这些与2020年的工程师或多或少相似的学生群体区别开来的教育经验。这种方法承认，工程专业的毕业生需要复杂的技能才能在新的全球经济中取得成功。这是与2020年工程师相关的技能的结合，而不是孤立的个人技能，这将确保毕业生能够应对未来的劳动力需求。迄今为止，对学生成果的研究是相互独立地研究学习成果，而不是对学生的学习进行全面研究。;该研究使用了从原型到生产的学生数据：由National Science赞助的2020年工程师研究的过程和条件基金会（NSF EEC-0550608）。来自美国的具有全国代表性的工程项目样本的工程专业学生回答了一项调查，该调查收集了有关其大学前的学术准备和社会人口统计学特征，其工程项目中的课程和课外经验以及他们的自我评价的信息。与工程相关的能力。分析中仅使用了工程学大四学生的数据（n = 2,422）;;数据集中的五个工程学科（生物医学/生物工程，化学，土木，电气和机械工程）中的每个学科都在多个阶段进行了分析）。首先，聚类分析基于九项自我报告的学习成果，包括基本技能，设计技能，情境意识，跨学科的学习成果，得出了工程学前辈的类型（或分组）（针对所研究的五个工程学科中的一个进行分类，并进行“全部工程”分析）。能力和专业技能。其次，针对每个学科和五个学科的结合，制定了大学预科特征以及学生在大学的经历。通过使用方差分析，卡方分析和多项式逻辑回归，该阶段还确定了在成绩结果水平较高的学生群体与其他群体的学生之间的学生特征和大学经历的差异。第二阶段为第三阶段提供了信息，该阶段产生了使用大学前的特征和学生经验变量来预测聚类成员资格的简约模型;总体而言，研究结果表明，包括全套E2020学习成果在内的分析产生了有意义的类型区分不同工程领域的学生分组。调查结果表明，一部分学生（2020年的工程师）在所有学习成果上都表现出很高的技能和能力。这些是与2020年的工程师最相似的联邦政府和工业界寻求的毕业生。此外，独特的课程和共同课程体验使每个工程学科的E2020学生群体与同一学科的其他学生群体区别开来。这些发现对实践具有重要的意义，因为它们确定了一系列特定学科的，课堂内和课堂外的学习经验，这些经验似乎可以促进这种多维结果集的发展。总体而言，工程课程中更广泛的课程侧重于广泛的系统角度，这最能区分那些报告E2020成绩非常熟练的学生。研究结果还表明，应根据工程学科量身定制改善本科工程成果的策略。该研究通过开发一种可用于创建基于结果的类型学的技术来为实践和研究做出贡献，该技术可应用于任何一组学习结果。结果的图形表示将大量信息整合为易于访问的格式，以便研究结果可以指导从业人员和政策制定者，他们试图改善这种多维的本科工程学习成果集。研究的未来方向还讨论了影响E2020学习成果的组织环境的运作，以及整个分类学中学生的预期职业轨迹。

著录项

  • 作者

    Knight, David B.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Education Sciences.;Education Higher.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 267 p.
  • 总页数 267
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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