Our team seeks to improve peer feedback on student presentations. Many college courses require students to give in-class presentations. This raises a number of challenges. Students who watch the presentation are typically not participating actively. Professors can be overwhelmed trying to provide feedback while also managing the class. The presenting students would benefit from receiving more feedback than the professor can provide.

This project introduces PeerPresents, an in-class peer feedback system we developed to improve the feedback process for in-class presentations. Our real-time interactive website allows students to provide feedback according to the presenting team’s needs, then encourages the presenting team to digitally organize and reflect on the comments they got from their peers. This reflection interface enables learners to turn peer feedback into actionable next steps for their project.

We are currently using this system to ask deeper questions about how students learn from the peer feedback process, both in receiving feedback from others and by giving feedback to their peers. We are investigating how students reflect on the comments they receive, how this reflection enhances student learning, and how to teach students to meaningfully reflect on peer feedback. We are also investigating how to help students learn to be expert critiquers and recognize when they provide helpful or unhelpful feedback. The PeerPresents system also supports instructors through the peer feedback process. Our system provides research opportunities to understand instructor motivations for using peer feedback in their class and what instructors need to make peer feedback more effective.

Use Our Tool

If you are an instructor who wants to use PeerPresents in your classroom, please email Amy Cook to learn more.

Get Involved

We are looking for undergraduates to help develop the tool, implement classroom studies, and analyze data. Ideal developer candidates will be familiar with Javascript, Node.js, CSS, SQL. Ideal data analysis candidates will be familiar with qualitative data analysis methods and R. You will have the opportunity to work with experts in HCI and learning science and to creatively influence the direction of the research!

If interested, please email your resume to Amy Cook.


Amy Cook
Jessica Hammer
Steven Dow