Research

Our CS Bridge faculty love to teach! They strive constantly to provide the best teaching experiences they can. Our active research agenda seeks to improve how we teach, depending on where we teach.

Our research activities span the following areas:

  1. Designing an international in-person CS curriculum which is open-source, high-quality and works around the world.

  2. Developing new methods for spreading teaching know-how across borders.

 

In order to make our programs more effective in each and every country we teach, we seek ways to import and adjust our curriculum to fit the local language and culture. We have developed many tools to assist in making our program as portable and accessible as possible.

Our open-source curriculum–composed primarily of programming assignments–is designed to be interactive, inspiring, and relatable to students. Given that the experiences of students vary from one country to the next, we design our curriculum as exploratory projects with minimal text, and we translate each and every page to fit the local language. We then take the extra step to ensure that any cultural references within assignments match the correct context.

In many host countries, we first rewrite the assignments to match cultural and historical accuracy. We then use an automatic translation tool to translate our material into the local language. Finally, we work closely with the local teaching team to adjust for colloquialisms and technical jargon.

The tools we use to provide our students the best experience in CS Bridge are open-source and available to the public.

Curriculum, tools, and Localization

 

As educators, we care that CS Bridge first and foremost benefits the students. We study how student participation in our program improves student confidence with computer science, boosts interest in STEM-related fields, and transforms learning mindset.

We collect fine-grained data that snapshots student code as they work through projects during CS Bridge. Coupled with pre and post surveys of confidence, belonging, and technical knowledge, we have a holistic understanding of our student body in each and every program. We then compare this data cross-culturally to look for similarities and differences among students from different socioeconomic contexts.

Through close collaboration with host universities, we grow a local section-leading team composed of undergraduate-level students with experience in computer science. These students not only become near-peer mentors for high school-level students, but also they experience a leadership role by teaching computer science. In our research, we emphasize undergraduate learning and growth and try to understand how section leaders perceive the field of computer science and their own future in leadership and in education.

Education for students and new teachers

 

Code in Place

  1. Chris Piech, Ali Malik, Kylie Jue, and Mehran Sahami. “Code in Place: Online Section Leading for Scalable Human-Centered Learning,” in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, (Virtual) USA, 2021.

Course-in-a-Box

  1. Chris Piech, Lisa Yan, Lisa Einstein, Ana Saavedra, Baris Bozkurt, Eliska Sestakova, Ondrej Guth, and Nick McKeown. “Co-Teaching Computer Science Across Borders: Human-Centric Learning at Scale,” in Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S ’20), June 2020. Awarded Best Paper.  

  2. Human Languages in Source Code: Auto-Translation for Localized Instruction. Preprint.

Other publications in Computer Science education, authored by our team members:

  1. Noah Arthurs, Ben Stenhaug, Sergey Karayev, Chris Piech. “Grades are not Normal: Improving Exam Score Models Using the Logit-Normal Distribution,” in Proceedings of the 12th International Conference on Educational Data Mining (EDM). July 2019.

  2. Lisa Yan, Nick McKeown, and Chris Piech. “The PyramidSnapshot Challenge: Understanding student process from visual output of programs,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2019.

  3. Lisa Yan, Annie Hu, and Chris Piech. “Pensieve: Feedback on coding process,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2019.

  4. Mike Wu, Milan Mosse, Noah Goodman, Chris Piech. “Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference,” in Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI), January 2019.

  5. Mehran Sahami. “Paving a path to more inclusive computing,” in ACM Inroads 9, 4, p. 85-88, November 2018.

  6. Chris Piech and Chris Gregg. “BlueBook: Secure, Electronic Computer Science Exams,” in Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2018.

  7. Lisa Yan, Nick McKeown, Mehran Sahami, and Chris Piech. “TMOSS: Using Intermediate Assignment Work to Understand Excessive Collaboration in Large Classes,” in Proceedings of The 49th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2018.

Publications