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.
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Beyond instructional practices: Characterizing learning environments that support students in explaining chemical phenomena
Many conversations surrounding improvement of large‐enrollment college science, technology, engineering & mathematics (STEM) courses focus primarily (or solely) on changing instructional practices. By reducing dynamic, complex learning environments to collections of teaching methods, we neglect other meaningful parts of a course ecosystem (e.g., curriculum, assessments). Here, we advocate extending STEM education reform conversations beyond “active versus passive learning.” We argue communities of researchers and instructors would be better served if what we teach and assess was discussed alongside how we teach. To enable nuanced conversations about the characteristics of learning environments that support students in explaining phenomena, we defined a model of college STEM learning environments which attends to the intellectual work emphasized and rewarded on exams (i.e., assessment emphasis), what is taught in whole‐class meetings (i.e., instructional emphasis), and how those meetings are enacted (i.e., instructional practices). We subsequently characterized three distinct chemistry courses and qualitatively examined the characteristics of chemistry learning environments that effectively supported students in explaining why a beaker of water warms as a white solid dissolves. Furthermore, we quantitatively investigated the extent to which measures of incoming preparation explained variance in students’ explanations relative to enrollment in each learning environment. Our findings demonstrate that learning environments that effectively supported learners in explaining dissolution emphasized how and why salts dissolve in‐class and on assessments. Changing teaching methods in an otherwise traditionally structured course (i.e., a course organized by topics that primarily assesses math and recall) did not appear to impact the sophistication of students’ explanations. Additionally, we observed that learning environment enrollment explained substantially more of the variance observed in students’ explanations than measures of precollege math preparation. This finding suggests that emphasizing and rewarding the construction of causal accounts for phenomena in‐class and on assessments may support more equitable achievement.
more » « less- Award ID(s):
- 1725520
- NSF-PAR ID:
- 10364698
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Research in Science Teaching
- Volume:
- 59
- Issue:
- 5
- ISSN:
- 0022-4308
- Page Range / eLocation ID:
- p. 841-875
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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