CAREER: Assessing and Improving Online Learning Spaces for Diverse and High-Attrition Engineering Students

评估和改进在线课程为工程本科生从不同的背景增加保留和成就的妇女，少数民族和高损耗群体在工程是最重要的国家战略利益，在线学习已成为一个流行的战略，以扩大访问。然而，有限的证据表明，在线课程适用于工程类的不同学生群体。 事实上，其他学科的早期研究结果表明，虚拟教学加剧了成绩差距。这项建议推动研究和实践，以更好地为所有工程专业的学生在三个主要方面。首先，它独立地分析了代表性不足或高损耗的学生群体，而不是研究整个班级的平均影响。其次，它跟踪学生的个人行为，以更好地解释学生成功的差异，并更好地推荐为独特的学生量身定制的支持系统。第三，它在五个不同的背景下研究本科生在线和混合学习，包括两个国际网站，以大大扩展可以为美国本科生工程提供信息的工具范围，特别是在大型公立大学环境中。这是通过调查员的研究和合作，在不同的机构四个介绍性工程课程。加强数字教育工具，以更好地支持独特的学生群体，特别是那些有高流失率的学生，可以满足国家对高素质和多样化的工程劳动力日益增长的需求。在线学习作为获得STEM教育的政策工具已经比比皆是-尽管事实上，有很少或矛盾的证据表明，在线学习，如目前所实行的，是增加机会、公平或成就的有效工具。估计差异效应的研究工作很少，很少有研究集中在高损耗的高中后学生在工程的高需求学科的在线学习。这项建议的主要目标是，首先，在现场仔细检查的实施，行为和经验的不同的子组，特别是高损耗的学生，在网上/混合环境中的本科工程类;和，第二，批判性地评估断言，网上/混合结构是显着相关的成就，这些重要的子组。这导致了四个研究问题：（1）在线和混合学习工具和结构（例如，MOOC，翻转教室）持续高消耗学习者人口统计？，(2)哪些在线结构和行为显著（实际和统计）预测这些学生的更高成就？(3)学生行为和结果的哪些差异可能与教学所处的制度环境有关或由其介导？，（4）对未来的研究设计和教育政策有何启示？这些问题将使用跨国多案例方法来回答，将准实验方法应用于来自不同机构背景的四门在线课程。在每项研究中，先进的回归技术被应用于了解个别学生的背景，材料的访问和在线行为如何预测成绩。具有国家和国际代表性的二手数据的背景研究将使特定课程的研究结果成为背景。定性访谈将补充定量调查结果。结果将告知四个学习课程和PI在普渡大学的第一年工程课程在五年项目生命周期的迭代改进。

Evaluating and Improving Online Courses For Engineering Undergraduates from Diverse Backgrounds Increasing the retention and achievement of women, minorities, and high attrition groups in engineering is of paramount national strategic interest, and online learning has emerged as a popular strategy for expanding access. However, there is limited evidence that online courses work for the diverse groups of students in engineering classes. In fact, early findings from other disciplines suggest that virtual instruction exacerbates achievement gaps. This proposal pushes research and practice to better serve all of the varieties of engineering students in three major ways. First, it analyzes underrepresented or high attrition student groups independently, rather than studying average effects for a whole class. Second, it tracks individual student behaviors to better explain differences in student success and better recommend support systems that are tailored to unique students. And, third, it studies undergraduate online and blended learning in five widely varying contexts, including two international sites, to greatly expand the spectrum of tools that can inform undergraduate engineering in the United States, in particular, in a large public university setting. This is achieved through the investigator's study of and collaboration with four introductory engineering courses at diverse institutions. Enhancing digital educational tools to better support unique groups of students, especially those that have high rates of attrition, can fulfill the nation's growing need for a highly qualified and diverse engineering workforce.Online learning as a policy tool for access to STEM education has abounded - despite the fact that there is minimal or contradictory evidence that online learning, as currently practiced, is an effective instrument for increasing access, equity, or achievement. Research efforts to estimate differential effects are few, and few studies focus on online learning for high attrition post-secondary students in the high need discipline of engineering. The primary goals of this proposal are, first, to carefully examine in situ the implementation, behaviors, and experiences of diverse sub-groups, in particular, high-attrition students, in online/blended environments in undergraduate engineering classes; and, second, to critically evaluate the assertion that online/blended structures are significantly related to achievement for these important sub-groups. This leads to four research questions: (1) Are the estimated quantitative effects of online and blended learning tools and structures (e.g., MOOCs, flipped classrooms) persistent for high attrition learner demographics?, (2) What online structures and behaviors significantly (practically and statistically) predict higher achievement for these students?, (3) What differences in student behaviors and outcomes might be related to or mediated by the institutional context in which instruction is taking place?, and (4) What are the implications for future research design and educational policy? These questions will be answered using a cross-national multi-case approach, applying quasi-experimental methods to four online courses from diverse institutional contexts. In each study, advanced regression techniques are applied to understand how individual students' backgrounds, access of materials, and online behaviors predict achievement. Background study with nationally-and internationally-representative secondary data will contextualize course-specific findings. Qualitative interviews will complement quantitative findings. Results will inform iterative improvements of the four study courses and the PI's first-year engineering course at Purdue over the five-year project lifecycle.