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

评估和改进为不同背景的工程学本科生开设的在线课程增加女性、少数族裔和高流失率群体在工程学领域的留住和成就，这是至关重要的国家战略利益，在线学习已成为扩大入学机会的一种流行战略。然而，有有限的证据表明，在线课程适用于工程类课程中的不同学生群体。事实上，来自其他学科的早期研究结果表明，虚拟教学加剧了成绩差距。这项建议在三个主要方面推动研究和实践更好地服务于所有不同类型的工科学生。首先，它独立分析代表性不足或高流失率的学生群体，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，以便更好地解释学生成功的差异，并更好地推荐为独特学生量身定做的支持系统。第三，它在包括两个国际网站在内的五个差异很大的背景下研究本科生的在线学习和混合学习，以极大地扩大可以为美国本科生工程提供信息的工具的范围，特别是在大型公立大学环境中。这是通过调查员研究不同机构的四门工程学入门课程并与之合作来实现的。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--尽管几乎没有或相互矛盾的证据表明，正如目前的做法一样，在线学习是增加机会、公平或成就的有效工具。对差异效应的估计研究很少，很少有研究集中在高要求工程学科的高流失率大专学生的在线学习上。这项建议的主要目标是，首先，仔细检查不同分组的实施、行为和经验，特别是在本科工程类在线/混合环境中的高流失率学生；第二，批判性地评估在线/混合结构与这些重要分组的成绩显著相关的断言。这就引出了四个研究问题：(1)在线和混合学习工具和结构(如MOOC，翻转课堂)的估计数量效应是否对高自然流失率的学习者人口结构持续存在？(2)哪些在线结构和行为显著(实际上和统计上)预测了这些学生的更高成就？(3)学生行为和结果的差异可能与进行教学的机构背景有关或受其影响？(4)对未来的研究设计和教育政策有何影响？这些问题的答案将使用跨国多案例方法，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，应用先进的回归技术来了解个别学生的背景、材料的获取和在线行为是如何预测成绩的。具有国家和国际代表性的二手数据的背景研究将结合具体课程的研究结果。定性访谈将补充定量结果。结果将在五年的项目生命周期内为四个学习课程和PI在普渡大学的第一年工程课程的迭代改进提供信息。

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.