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Updated: Aug 5, 2026
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Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience
Published on: November 15, 2024
Breaking Barriers in Large Classes: Using Cloud Computing and Computational Biology to Implement Research-Inspired
Christopher E Berndsen1, Kathryn E Shenk2, Alyssa Young2
1Department of Chemistry and Biochemistry, James Madison University, Harrisonburg, Virginia, USA.
Summary
Large biochemistry courses can now offer research experiences through a novel, cloud-based project. This approach enhances student skills in protein analysis and scientific database use, overcoming access barriers.
Area of Science:
- Biochemistry Education
- Computational Biology
- Undergraduate Research
Background:
- Course-based undergraduate research experiences (CUREs) are valuable but difficult to implement in large lectures.
- Large class sizes and content demands pose significant challenges to integrating research projects.
Purpose of the Study:
- To develop and evaluate a research-inspired project for a large-enrollment (70+ students) Biochemistry lecture course.
- To complement existing curriculum with hands-on experience on novel research projects.
- To overcome access barriers in large courses using cloud-based computational tools.
Main Methods:
- Implemented a semester-long, four-module project focused on protein structure and function.
- Utilized cloud-based computational techniques, specifically Google Colaboratory (Colab), for software accessibility.
- Integrated information literacy and data management training with a science librarian.
- Students conducted individual projects on novel protein variants and their functional effects.
Main Results:
- Over 250 students participated since Fall 2024.
- Student self-assessments indicated increased confidence in analyzing molecular structures and linking mutations to protein function.
- Students demonstrated improved skills in using scientific databases like UniProt.
Conclusions:
- Research-inspired curricula can be successfully integrated into large, content-heavy lecture courses.
- Accessible cloud-computing resources are key to overcoming implementation challenges.
- This model effectively enhances undergraduate biochemistry students' research competencies.
