Scholarship

I am primarily interested in Computer Science Education research and have published in the Journal of Educational Computing Research and ACM InRoads, as well as presented at conferences such as the ACM SIGCSE Technical Symposium and the Conference on International Computing Education Research.

Selected Publications

  • [1] George Veletsianos, Bradley Beth, Calvin Lin, and Gregory Russell. Design Principles for Thriving in Our Digital World, a High School Computer Science Course. Journal of Educational Computing Research, 54:443-461, July 2016. [Link, PDF]
  • [2] George Veletsianos, Bradley Beth, and Calvin Lin. CS Teacher Experiences with Educational Technology, Problem-Based Learning, and a CS Principles Curriculum. In Proceedings of the 47th ACM Technical Symposium on Computer Science Education, SIGCSE ’16, Memphis, TN, March 2016. ACM. [Link, PDF]
  • [3] Bradley Beth, Calvin Lin, and George Veletsianos. Training a diverse computer science teacher population. ACM Inroads, 6(4):94-97, November 2015. [Link, PDF]
  • [4] Elynn Lee, Victoria Shan, Bradley Beth, and Calvin Lin. A Structured Approach to Teaching Recursion Using Cargo-Bot. In Proceedings of the Tenth Annual International ACM Conference on International Computing Education Research, ICER ’14, pages 59-66, Glasgow, Scotland, UK, August 2014. [Link, PDF]
  • [5] Joe Tessler, Bradley Beth, and Calvin Lin. Using Cargo-Bot to Provide Contextualized Learning of Recursion. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research, ICER ’13, pages 161-168, San Diego, San California, August 2013. ACM. [Link, PDF]

Projects

Some resources I have created for teaching different concepts in computer science courses are listed below:

ACM Ethics Case Studies
This project is a retooling an ACM reference case study as a non-linear “choose your own outcome” narrative. [Link, PDF, Source, Release]
RPS_BlackBox
This project is designed to give students an environment to explore the idea of basic black box testing by manually creating hypothetical input/output test cases and comparing them to actual outcomes. [Source, Release]