Adapting Lab Courses for Virtual Learning on Benchling

Student-Focused Learning with Benchling

While there are a variety of active learning approaches to use in biology education, we generally describe two strategies that work well with Benchling’s platform and intend to expand upon both of these approaches with the designated course topics.

Problem-Based Learning

Design molecular biology scenarios with key tasks and deliverables from your students. An example course module, “The Basics of Primer Design for PCR,” explores how you can prompt students to design primers and model PCR for specific genes in an organism. You can adapt this approach for a number of molecular biology techniques, where open-ended prompts will allow students to develop problem-solving skills as a scientist.

Populate experimental datasets that your class can access through Notebook templates and sequence imports. Have students analyze these materials and report their own interpretations of the data. Another module below, “In Silico Analysis of DNA and Protein Sequences,” describes how you can use this method to introduce students to bioinformatic analysis. Given a set of sequences, students will determine sequence homology, validate potential ORFs, and identify putative proteins.

Inquiry-Based Learning

Compile various laboratory techniques, and have students investigate and evaluate techniques they’re interested in. In the example course module, “Investigating Molecular Cloning Methods: Restriction, Gibson, and Golden Gate,” students drive their own learning through investigating a particular cloning strategy. Having students teach a technique to others will allow them to master it for themselves.

Model the scientific process by asking students to design mock experiments and include methodology, materials, and references. The module “How to Keep an Electronic Lab Notebook (ELN)” showcases the power of ELNs as students develop protocols and analyze experimental data. Students can read and analyze peer-reviewed literature, evaluate experimental workflows used to obtain published data, and recreate protocols as if they were performing experiments themselves.