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Foundational engineering courses are critical to student success in engineering programs. The conceptually challenging content of these courses establishes the requisite knowledge for future classes. Thus, it is no surprise that such courses can serve as barriers or gatekeepers to successful student progress through the undergraduate curriculum. Although the difficulty of the courses may be necessary, often other features of the course delivery such as large class environments or a few very high-stakes assessments can further exacerbate these challenges. And especially problematic, past studies have shown that grade penalties associated with these courses and environments may disproportionately impact women. On the faculty side, institutions often turn to non-tenure track instructional faculty to teach multiple sections of foundational courses each semester. Although having faculty whose sole role is dedicated to quality teaching is an asset, benefits would likely be maximized when such faculty have clear metrics for paths to promotion, some autonomy and ownership regarding the curriculum, and overall job satisfaction. However, literature suggests that faculty, like students, note ill effects from large classes, such as challenges connecting and building rapport with students and having time to offer individualized feedback to students. Our NSF IUSE project focuses on instructors of large foundational engineering students with the belief that by better understanding the educational environment from their perspective we can improve the quality of the teaching and learning environment for all engineering students. Our project regularly convenes faculty teaching an array of core courses (e.g,. Mathematics, Chemistry, Mechanics, Physics) and uses insights from these meetings and individual interviews to identify possible leverage points where our project or the institution more broadly might affect change. Parallel to this effort, we have been working with data stewards on campus to gain access to institutional data (e.g., student course and grade histories, student evaluations of faculty teaching) to link and provide aggregate deidentified results to faculty to feed more information in to their decision-making. We are demonstrating that regular engagement between faculty and institutional leaders around analyzed and curated data is essential to continuous and systematic improvement. Efforts to date have included building an institutional data explorer dashboard (e.g., influences of pre-requisite courses on future courses) and drafting reports to be sent to department heads and associate deans which gather priorities identified in the first year of our research. For example, participating instructors identified that clarity of promotion paths across non-tenure track teaching faculty from different departments varied greatly, and the institution as a whole could benefit from clarified university-wide guidance. While some findings may be institution-specific (NSF IUSE Institutional Transformation track), as a large public research institution, peer-institutions with high engineering enrollments often face similar challenges and so findings from our change efforts potentially have broad applicability. 

Engineering students develop competencies in fundamental engineering courses (FECs) that are critical for success later in advanced courses and engineering practice. Literature on the student learning experience, however, associate these courses with challenging educational environments (e.g., large class sizes) and low student success rates. Challenging educational environments are particularly prevalent in large, research-intensive institutions. To address concerns associated with FECs, it is important to understand prevailing educational environments in these courses and identify critical points where improvement and change is needed. The Academic Plan Model provides a systematic way to critically examine the factors that shape the educational environment. It includes paths for evaluation and adjustment, allowing educational environments to continuously improve. The Model may be applied to various levels in an institution (e.g., course, program, college), implying that a student’s entire undergraduate learning experience is the result of several enacted academic plans that are interacting with each other. Thus, understanding context-specific factors in a specific educational environment will yield valuable information affecting the undergraduate experience, including concerns related to attrition and persistence. In order to better understand why students are not succeeding in large foundational engineering courses, we developed a form to collect data on why students withdraw from certain courses. The form was included as a requirement during the withdrawal process. In this paper, we analyzed course withdrawal data from several academic departments in charge of teaching large foundational engineering courses, and institutional transcript data for the Spring 2018 semester. The withdrawal dataset includes the final grades that students expected to receive in the course and the factors that influenced their decision to withdraw. Institutional transcript data includes demographic information (e.g., gender, major), admissions data (e.g., SAT scores, high school GPA), and institutional academic information (e.g., course grades, cumulative GPA). Results provide a better understanding of the main reasons students decide to withdraw from a course, including having unsatisfactory grades, not understanding the professor, and being overwhelmed with work. We also analyzed locus of control for the responses, finding that the majority of students withdrawing courses consider that the problem is outside of their control and comes from an external source. We provide analysis by different departments and different specific courses. Implications for administrators, practitioners, and researchers are provided. 

The literature in engineering education and higher education has examined the implications of course-taking patterns on student development and success. However, little work has analyzed the trajectories of students who need to retake courses in the curriculum, especially those deemed to be fundamental to a student’s program of study, or the sequences of courses. Sequence analysis in R was used to leverage historical transcript data from institutional research at a large, public, land-grant university to visualize student trajectories within the individual courses – with attention to those who re-enrolled in courses – and the pathways students took through a sequence of courses. This investigation considered students enrolled in introductory mechanics courses that are foundational for several engineering majors: Statics, Dynamics, and Strength of Materials (also called Mechanics of Deformable Bodies). This paper presents alluvial diagrams of the course-taking sequences and transition matrices between the different possible grades received upon subsequent attempts for the Mechanics core courses to demonstrate how visualizing students’ paths through sequences of classes by leveraging institutional data can identify patterns that might warrant programs to reconsider their curricular policies. 

This Work-In-Progress research paper presents preliminary results and next steps of a study that aims to identify institutional data and resources that instructors find helpful in facilitating learning in large foundational engineering courses. The work is motivated by resource-driven compromises made in response to increasing engineering student populations. One such compromise is teaching some courses (usually foundational courses taken by students across multiple disciplines) in large sections, despite research suggesting that large class environments may correspond with unfavorable student learning experiences. Examples of courses often taught in large class environments are mathematics, physics, and mechanics. We are currently working with a cohort of instructors of foundational engineering courses as part of an NSF Institutional Transformation project. We have collected qualitative data through semi-structured interviews to explore the following research question: What data and/or resources do STEM faculty teaching large foundational classes for undergraduate engineering identify as being useful to enhance students' experiences and outcomes a) within the classes that they teach, and b) across the multiple large foundational engineering classes taken by students? Our inquiry and analysis are guided by Lattuca and Stark's Academic Plan Model. Preliminary analysis indicated that instructors would like more opportunities to interact and collaborate with instructors from other departments. These results will inform activities for our Large Foundational Courses Summit scheduled for Summer 2018 as part of the project. 

The drive to encourage young people to pursue degrees and careers in engineering has led to an increase in student populations in engineering programs. For some institutions, such as large public research institutions, this has led to large class sizes for courses that are commonly taken across multiple programs. While this decision is reasonable from an operational and resource management perspective, research on large classes have shown that students suffer decreased engagement, motivation and achievement. Instructors, on the other hand, report having difficulty establishing rapport with their students and a growing inability to monitor students’ learning gains and provide quality individualized feedback. To address these issues, our project draws from Lattuca and Stark’s Academic Plan model, which incorporates a thorough consideration of factors influencing curricular activities that can be applied at the course, program, and institutional levels, and assumes that instructors are key actors in curriculum development and revision. We aim to revitalize feedback loops to help instructors and departments continuously improve. Recognizing that we must understand both individual and systems level perspectives, we prioritize regular engagement between faculty and institutional support structures to collaboratively identify problems and systematically establish continuous improvement. In the first phase of this NSF IUSE Institutional Transformation project, we focus on specifically prompting and studying the experiences of 8 instructors of foundational engineering courses usually taught in large class sizes across 4 different departments at a large public research institution. We collected qualitative data (semi-structured interviews, reflective journals, course-related documents) and quantitative data (student surveys and institution-provided transcript data) to answer research questions (e.g., what data do faculty teaching large foundational undergraduate engineering courses identify as being useful so that they may enhance students’ experiences and outcomes within the classes that they teach and across students’ multiple large classes?) at the intersection of learning analytics and faculty change. The data was used as a baseline to further refine data collection protocols, identify data that faculty consider meaningful and useful for managing large foundational engineering courses, and consider ways of productively leveraging institutional data to improve the learning experience in these courses. Data collection for the first phase is ongoing and will continue through the Spring 2018 semester. Findings for this paper will include high-level insights from Fall interviews with instructors as well as data visualizations created from the population-level data characterizing student performance in the foundational courses within the context of pre-college characteristics (e.g., SAT scores) and/or other academic outcomes (e.g., major switching within or out of engineer, degree attainment).