Impacts of Collaborative Learning in STEM (2020-2021)
Collaborative learning is an evidence-based instructional strategy that deepens student learning by facilitating engaging classroom discussions among students. These cooperative activities are at the core of active learning, a pedagogical method whereby students perform activities for the purpose of discovering, processing and applying information. While active learning increases performance for most students in STEM fields, underrepresented students show the largest positive effect. In addition, these teaching interventions completely close the performance gap between first generation college students and continuing generation students. Why, then, is active learning more effective for STEM students from disadvantaged backgrounds? The interactive and interdependent nature of active learning helps to increase the sense of community in the classroom, an effect that may be especially important to the success of at-risk student populations.
Why, then, is active learning more effective for STEM students from disadvantaged backgrounds? The interactive and interdependent nature of active learning helps to increase the sense of community in the classroom, an effect that may be especially important to the success of at-risk student populations.
This project will investigate the impacts of collaborative learning on course-related behaviors and enthusiasm for course content in undergraduate STEM classrooms at Duke University. Team members will aim to identify teaching practices in STEM classrooms that improve retention rates for all students, close achievement gaps, generate enthusiasm for course content and promote equity in the classroom.
Last year’s team designed a survey, which included self-assessments of motivation, belonging, support and self-efficacy, for a qualitative analysis of the effects of collaborative learning on students in STEM classes through the Trinity College of Arts & Sciences. The main goal of the 2020-2021 project is to expand the study of collaborative learning practices to include online learning formats under COVID-19 conditions. The team will compare the effects of collaborative learning in both online and in person class formats, and compare those to traditional lecture courses taught online and in person.
In addition, the team will analyze data collected to look for nuances and relationships among variables, including type of STEM course, demographics, gender, student class year and previous experience with collaborative learning.
Data to guide course enhancement and curriculum design; manuscripts for publication
Summer 2020 – Spring 2021
- Summer 2020 (optional): Learn about active learning pedagogies; carry out a literature review; identify analysis methods for survey data; draft abstracts; identify potential conferences and meetings for the upcoming academic year
- Fall 2020: Implement data analysis techniques for data collected previously; assess data to shape and submit an IRB to collect data from students enrolled in Pratt; prepare abstracts; identify conferences and workshops to attend
- Spring 2021: Present findings at national meetings; write a manuscript for publication
See earlier related team, Collaborative Learning in STEM: Impacts on Student Motivation, Retention and Self-efficacy (2019-2020).
Image: First-year engineering students tackle a service project during their EGR101L course, by Jared Lazarus/Duke University
- Elizabeth Bucholz, Pratt School of Engineering-Biomedical Engineering
- Dorian Canelas, Arts & Sciences-Chemistry
- Thomas Newpher, Arts & Sciences-Psychology and Neuroscience
- Minna Ng, Arts & Sciences-Psychology and Neuroscience
/graduate Team Members
Madelyne Huibregtse, Masters of Public Policy
/undergraduate Team Members
Matthew Long, Biology (AB)
Alina Perez, Computer Science (BS)
Isabella Swigart, Statistical Science (BS), Computer Science (BS2)
/yfaculty/staff Team Members
Jeffrey Forbes, Arts & Sciences-Computer Science