2026 ASEE Annual Conference & Exposition

Despite increasing efforts to improve student outcomes in STEM education, many faculty struggle with engagement, motivation, and academic performance in their classrooms. In this study, a STEM Faculty Learning Community investigated the impact of three classroom-based interventions—metacognitive prompts, real-time grade access, and peer performance context—on seven core student behaviors related to student involvement and learning outcomes.

Drawing on published research on metacognition, social comparison, and self-regulated learning, this work in progress aims to uncover how students’ cognitive and emotional responses to their academic progress can be leveraged to enhance their learning experiences and outcomes. This institution has a student body that is approximately 50% first generation and 50% low-income (Pell-eligible), and these students may face unique strengths and challenges in navigating STEM environments. The approaches, however, are applied to support all students in these courses.

Faculty identified seven core behaviors to observe:
1. Required assignment completion
2. Optional engagement (i.e. with online tools such as views on videos)
3. Recovery attempts (i.e. submitting assignments for bonus points)
4. Response to questions in class, or participating in in-class activities
5. Academic performance (grades)
6. Academic effort (displaying work or thought process)
7. Professor interaction (i.e. Engagement in office hours for help)

Research questions:
RQ1: How does encouraging students to think about their thinking (metacognition) impact the observation of seven core behaviors?
RQ2: How does access to real-time grades, progress tracking, and individual performance projections impact the observation of seven core behaviors?
RQ3: How does sharing grade histograms and peer performance impact the observation of seven core behaviors?

To minimize burden on students, this study employs low-impact data collection methods: (1) Learning Management System (LMS) analytics for assignment submissions, login frequency, video views; (2) Calculated optimistic and status quo outputs of predicted grades using custom software; (3) Classroom participation; (4) Brief embedded questions in student evaluation instruments; (5) Optional focus groups scheduled outside class time; (6) Demographic data from enrollment dashboards.

Quantitative data from the LMS will be analyzed using descriptive statistics and inferential tests (e.g., t-tests, ANOVA). Classroom observation data will be coded using a rubric aligned with the seven core behaviors. Survey responses will be analyzed using thematic coding and Likert-scale analysis.

This study contributes to the growing body of research on inclusive and dignity-affirming STEM pedagogy. By leveraging faculty collaboration through a learning community, the project models a scalable approach to pedagogical innovation. The approaches are applied to support all students in these courses, with a hope to benefit this (and other) institutions in enhancing STEM students’ motivation, engagement, and academic performance.