Studying Undergraduate Curricular Complexity for Engineering Student Success (SUCCESS)

随着对工程专业毕业生需求的增长，人们越来越有兴趣了解是什么阻碍了学生完成学位。并不是每个人都遵循同样的途径获得工程学位；目前的纵向研究表明，学生的亚群采取不同的路线完成或放弃他们的学业。因此，BPE项目寻求支持研究，以了解将学生从服务不足的社区驱逐出去的系统性障碍。这个项目将考察所有学生在攻读工程学位时必须克服的障碍，即课程本身。一个名为“课程分析”（Curricular Analytics）的新框架提供了一种思考如何分析工程课程的新方法。该框架量化了使课程变得“复杂”的课程特征，以便将它们与毕业率等结果联系起来。该项目将使用一个名为“多机构工程纵向发展数据库”（Multi-Institution Database for Engineering longitudinal Development，简称“中场”）的纵向数据集，该数据集包含了1987年至2018年美国21所大学本科生的近200万条记录，其中14.4%是工科学生。利用这些数据，将创建学生的课程学习轨迹，根据课程复杂性的衡量方法聚类以找到模式，并将其与跨工程学科的既定课程进行比较，然后分解为子群体。这个项目有可能使管理人员、课程设计者和顾问了解由教师和学生的偏差所定义的课程的复杂性如何影响不同学生群体的成功。该项目将从课程复杂性的角度来考察哪些类型的课程，如第一年的经验、入学模式和课程顺序，能够支持不同学生群体的保留和毕业。为了探索课程因素对不同学生造成的障碍，课程分析框架的措施将被应用于定量地捕捉课程的顺序和相互联系，并研究不同的学生途径——无论是编纂的还是经验丰富的。指导性的研究问题是：课程复杂性在以下阶层（机构、学科、入学模式、人口和途径）之间的变化是什么？以及课程复杂性在多大程度上与不同人口子群体的结果相关？该项目将结合课程复杂性、学生成果和来自多机构工程纵向发展调查数据库（中场）的课程学习数据，以了解扩大工程参与的课程因素，包括保留和学位完成。这个项目将涉及使用关联分析从学生的选课数据中创建轨迹。这些轨迹的课程复杂性将在感兴趣的层次上进行计算和分解。这些新数据将用于探索学科粘性和迁移率等生态系统指标的课程复杂性与课程复杂性之间的关系。将该数据库与课程复杂度框架相结合，可以为研究拓宽参与背景下工科学生学位获得与保留的纵向差异提供新的视角。目前，课程复杂性是一个具有描述和解释潜力的新生框架，例如将课程质量与课程复杂性度量相关联。实证结果可以通过向适当的行政利益相关者提供独特的报告，直接为所研究机构未来的课程修订和政策努力提供信息。通过将这些结果直接传播给机构利益相关者，该项目将利用中场的分解潜力，在课程和项目层面影响代表性不足的工程专业学生的包容性努力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As the demand for engineering graduates grows, there has been mounting interest in understanding what prevents students from completing their degrees. Not everyone follows the same pathway to an engineering degree; current longitudinal research suggests that subpopulations of students take different routes to either completing or abandoning their studies. Accordingly, the BPE program seeks to support research on understanding systematic barriers that push out students from underserved communities. This project will examine a barrier that all students must overcome when pursuing an engineering degree, the curriculum itself. An emerging framework called Curricular Analytics has provided a new method of thinking about how curricula in engineering can be analyzed. The framework quantifies features of the curriculum that make it “complex” such that they can be connected to outcomes like graduation rates. This project will use a longitudinal dataset called the Multi-Institution Database for Engineering Longitudinal Development (MIDFIELD), which contains nearly two million records from undergraduate students at 21 U.S. universities from 1987 through 2018 – 14.4 % of which are engineering students. Using these data, student course-taking trajectories will be created, clustered to find patterns based on measures of curricular complexity and compared to established curricula across engineering disciplines, then disaggregated into subpopulations. This project has the potential to empower administrators, curriculum designers, and advisors to understand how the complexity of the curriculum as defined by faculty and students’ deviances from it affects the success of different subpopulations of students. The project will use the perspective of curricular complexity to examine what kinds of programs such as first year experiences, enrollment models, and course sequencing that support the retention and graduation of different subpopulations of students.To explore what barriers curricular factors impose on different students, the Curricular Analytics framework’s measures will be applied to capture a curriclum’s sequencing and interconnectedness quantitatively and study diverse student pathways - both as codified and as experienced. The guiding research question is: what is the variation in curricular complexity among the following strata – institutions, disciplines, matriculation models, populations, and pathways – and to what extent does curricular complexity relate to outcomes for diverse population subgroups? This project will combine curricular complexity with student outcomes and course-taking data from the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to understand curricular factors in broadening participation in engineering, including retention and degree completion. This project will involve creating trajectories from student course-taking data using association analysis. The curricular complexity for these trajectories will be calculated and disaggregated across the strata of interest. These new data will be used to explore how curricular complexity for ecosystem metrics like discipline stickiness and migration yield are related to the complexity of a curriculum. The combination of the MIDFIELD database with the curricular complexity framework can bring a new perspective on differences in longitudinal studies on engineering student degree attainment and retention in the context of broadening participation. Currently, curricular complexity is a nascent framework with descriptive and explanatory potential, such as correlating program quality with curricular complexity measures. The empirical results can directly inform future curriculum revisions and policy efforts at the studied institutions through unique reports delivered to the appropriate administrative stakeholders. By disseminating these results directly to institutional stakeholders, The project will leverage MIDFIELD’s potential for disaggregation to impact inclusion efforts of underrepresented students in engineering at the curricular and programmatic level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.