2026 ASEE Annual Conference & Exposition

This Evidence-Based Practice full paper presents a study design and assessment framework for improving teaching and engagement in large-enrollment engineering courses.

Large classes show a lot of challenges for both teachers and students: how to maintain focus in crowded classrooms, how to coordinate teams of teaching assistants (TAs), how to assess students’ performance more fairly, and how to protect students' well-being. Our goal was to establish a practical improvement cycle that teachers could repeat each semester to align course practices with student expectations and experiences.

The study collects information from both faculty and students over time, using interviews and surveys at a large public research university. Phase 1 (completed) consisted of semi-structured interviews with 12 faculty who regularly teach courses enrolling 80–550 students across engineering and related STEM fields. From these interviews, we obtained feedback in five domains: classroom engagement, grading and feedback, TA coordination, technology use, and student support. In Phase 2, with IRB approval, we are now collecting student perspectives through two surveys—one at the start of the semester and another at the end. Together, these phases provide a comprehensive view of both instructor and student perspectives across the semester in these key areas.

Preliminary results highlight recurring instructor strategies. These include short in-class activities and polls that are tied to assessments, automation of routine tasks with targeted feedback, weekly TA meetings to align grading standards and clarify responsibilities, steady communication with students who fell behind during the semester through reminders and brief updates, and flexible policies that account for workload and mental health. Pre-semester student responses, collected under an approved IRB protocol prior to data collection, highlight the importance of transparent grading, predictable workload, timely feedback, and interactive activities when those activities contribute directly to grades. We observe strong alignment on transparency and feedback practices. There is partial alignment with in-class activities, since students prefer clearer grading connections. Finally, we find persistent gaps related to exam fairness and the appropriate use of AI tools.

The paper contributes three practical outputs. First, an alignment table that instructors can use in week one to set shared expectations with students. Second, a TA-calibration checklist that standardizes rubrics, grading timelines, and curve rules. Third, early-alert heuristics identify students at risk based on patterns like missed work, low quiz scores, or lack of participation, followed by a brief outreach template that offers quick support options such as office hours, worked examples, or a short practice set. The end-of-semester data (now in progress) will complete the first cycle and guide revisions to these tools. Together, the framework offers a replicable path to improve teaching quality and student engagement in large engineering courses while keeping a simple solution for busy teams.