Carnegie Mellon University
Carnegie Mellon Robotics Academy

Use educational affordances of robotics to create CS-STEM opportunities for all learners

Carnegie Mellon Robotics Academy  ›  Research

The Carnegie Mellon Robotics Academy conducts research at the intersection of robotics, learning sciences, and workforce development. We lead federally- and industry-funded research initiatives that explore:

  • Learning Systems & Assessment
    Designing measurable, standards-aligned robotics curricula and learning environments that promote programming fluency, systems thinking, and robotics literacy.

  • Workforce Development Pathways
    Building scalable, credentialed training programs—aligned with industry-recognized standards—that prepare learners for careers in advanced manufacturing, automation, and robotics.

  • Educational Technology & Learning Platforms
    Developing custom instructional software, virtual robotics simulators, and physical programming environments that enable scalable, hands-on practice—both in classrooms and online.

  • Collaborative Robotics & Game-Based Learning
    Investigating how “cobot games” and co-programmed robotic teammates can enhance motivation, problem-solving, and conceptual understanding through adaptive, interactive gameplay.

  • Applied STEM Learning
    Using robotics as a context to deepen understanding of math, science, and computer science concepts, while fostering durable skills through engineering design and experimentation.

Our work is informed by partnerships with schools, technical colleges, industry, and national research agencies. Outcomes include open-access curricula, teacher professional development, certification programs, and peer-reviewed studies that inform best practices in robotics education and workforce preparation.

Project Pages

Learning Systems & Assessment

  • Higashi, R., & Schunn, C. D. (2020). Perceived relevance of digital badges predicts longitudinal change in program engagement. Journal of Educational Psychology, 112(5), 1020–1041. 
  • Witherspoon, E., Higashi, R., Schunn, C. D., Shoop, R. "Attending to structural programming features predicts differences in learning and motivation in a virtual robotics programming curriculum" Journal of Computer Assisted Learning, , v.34(2), , 2018
  • Higashi, R., Schunn, C., Nguyen V., & Ososky, S. (2017). Coordinating Evidence Across Learning Modules using Digital Badges. In Sottilare, R., Graesser, A., Hu, X., and Goodwin, G. (Eds.). Design Recommendations for Intelligent Tutoring Systems: Volume 5 - Domain Modeling. Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9893923-9-6. 
  • Menekse, M., Higashi, R., Schunn, C., & Baehr, E. "Exploring the role of robotics teams? collaboration quality on team performance in a robotics tournament." Journal of Engineering Education , v.106(4), , 2017
  • Higashi, R., Schunn, C., Flot, J (May 2017) Different underlying motivations and abilities predict student versus teacher persistence in an online course. Education Tech Research Dev DOI 10.1007/s11423-017-9528-z 
  • Abramovich, S., Schunn, C.D., Higashi, R. (2013) Are Badges Useful in Education?: it depends upon the type of badge and expertise of Learner. Educational Technology Research & Development, March 2013. DOI: 10.1007/s11423-013-9289-2. 
  • Higashi, R., Abramovich, S., Shoop, R., Schunn, C.D.(2012, June) The Roles of Badges in the Computer Science Student Network. 2012 GLS Conference 
  • Abramovich, S., Higashi, R., Hunkele, T. Schunn, C.D., Shoop, R. (2011, July) Achievement Systems to Boost Achievement Motivation. 2011 GLS Conference 

Workforce Development Pathways

  • Higashi, Nguyen & Flot, Results of National Association of Manufacturers Member Interviews around ML and AI
  • Andrew, Megan, Timothy Marler, Jesse Lastunen, Hannah Acheson-Field, and Steven W. Popper, How Can the United States Prepare a Workforce for Advanced Manufacturing Using Robotics?. Advanced Robotics Manufacturing Institute in Pittsburgh, 2020. 
  • Andrew, Megan, Timothy Marler, Jesse Lastunen, Hannah Acheson-Field, and Steven W. Popper, An Analysis of Education and Training Programs in Advanced Manufacturing Using Robotics. Advanced Robotics for Manufacturing Institute, Pittsburgh, Pa., 2020. 

 

Educational Technology & Learning Platforms

  • Witherspoon, E., Higashi, R., Schunn, C., Shoop, R., Baehr, E. (October 2017) Developing Computational Thinking through a Virtual Robotics Programming Curriculum. ACM Transactions on Computing Education, Vol. 18, No. 1, Article 4. 
  • Liu, A., Schunn, C. D., Flot, J., & Shoop, R. (2013) The role of physicality in rich programming environments. Computer Science Education, 23(4), 315-331.
  • Flot, J., Shoop, R (November 2013) Foregrounding Math, Engineering, and Computer Science using Robotics. The presentation was given at the Technology Education and Engineering Association of Pennsylvania Annual Conference, Camp Hill Pennsylvania.
  • Flot, J., Shoop, R (November 2013) Teaching Programming with Robot Virtual Worlds. The presentation was given at the Technology Education and Engineering Association of Pennsylvania Annual Conference, Camp Hill Pennsylvania.
  • Liu, A., Newsom, J., Schunn, C., Shoop, R. Learn to program in half the time!. Robot Magazine, 49-51. 
  • Liu, A., Newsom, J., Schunn, C., Shoop, R. Students Learn Programming Faster through 7. Robotic Simulation. Tech Directions, 16-19.
  • Flot, J., Lui, A., Schunn, C., Shoop, R. (November 2012). Learning How to Program via Robot Simulation.  Robot Magazine, 68-70. 
  • Atwood, T., Shoop, R. Carnegie Mellon Launches a Mega Million Dollar Robotics Education Initiative. Robot Magazine, 70-71. 
  • Shoop, R. (2011, May). FIRE Unveils Robot Virtual World Games.  Robot Magazine, 78-81. 
  • Shoop, R. (2011, January) Computer Science Student Network Project. Presented at the Computing Education for the 21st Century (CE21) meeting, New Orleans 
  • Higashi, R., Shoop, R. (2011, November) Organizational Expectations Presented to Propel School System Teachers and Administrators, Robot Algebra Partnership Kickoff 

Collaborative Robotics & Game-Based Learning

  • Harpstead, E. (2023). Learning within Gameplay Loops: Considering Adaptive Educational Technology from a Game Design Lens. Proceedings of the 25th International Conference on Human-Computer Interaction, to appear.
  • Solyst, J., Uchidiuno, J. O., Harpstead, E., Kemper, J., & Higashi, R. (2023). Comparative design-based research: How afterschool programs impact learners’ engagement with a video game codesign. To appear in the Proceedings of the International Conference of the Learning Sciences (ICLS), June 10-15, 2023, Montreal, Canada.
  • Higashi, R., Harpstead, E., Solyst, J., Kemper, J., Odili Uchidiuno, J., & Hammer, J. (2021, October). The Design of Co-Robotic Games for Computer Science Education. In Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (pp. 111-116).

Applied STEM Learning

  • Flot, J., (2018, July). Developing Computational Thinking through a Virtual Robotics Programming Curriculum. Computer Science Teachers Association. 
  • Alfieri, L., Higashi, R., Shoop, R., Schunn, C.D., (2015, February). Case studies of a robot-based game to shape interests and hone proportional reasoning skills. International Journal of STEM Education. 
  • King, S., Stein, M., Schunn, C.D., (2012, May). Designing Educative Guides: Reconceptualizing Teacher’s Role in Teacherless Cognitive Tutor-based Robotics Instruction. Paper presented at the 2012 annual meeting of the American Society for Engineering Education, Vancouver, BC. 
  • Silk, E. M. (2011). Resources for learning robots: Environments and framings connecting math in robotics(Doctoral dissertation, University of Pittsburgh). Available from D-Scholarship at the University of Pittsburgh. (No. 8607) 
  • Silk, E. M., Higashi, R., & Schunn, C. D. (2011, June). Resources for robot competition success: Assessing math use in grade-school-level engineering design. Paper to be presented at the annual meeting of the American Society for Engineering Education, Vancouver, BC, Canada. 
  • Silk, E. M., & Schunn, C. D. (2011, June). Calculational versus mechanistic mathematics in propelling the development of physical knowledge. Paper to be presented at the 41st annual meeting of the Jean Piaget Society, Berkeley, CA, USA. 
  • Silk, E. M., & Schunn, C. D. (2011, April). Resources for learning robots: Facilitating the incorporation of mathematical models in students’ engineering design strategies.Paper to be presented at the annual meeting of the American Educational Research Association, New Orleans, LA, USA. 
  • Silk, E. M., Schunn, C. D., Shoop, R., & Stein, M. K. (2011, March). The Robot Algebra Project. Poster presented at the eighth annual NSF ITEST Summit, Arlington, VA, USA. 
  • Silk, E. M., Higashi, R., Shoop, R., & Schunn, C. D. (2010). Designing technology activities that teach mathematics. The Technology Teacher, 69 (4), 21-27. 
  • Silk, E. M., Schunn, C. D., & Shoop, R. (2009). Synchronized robot dancing: Motivating efficiency & meaning in problem-solving with robotics. Robot Magazine, 17, 74-77. 
  • Silk, E. M., & Schunn, C. D. (2008, June).Using robotics to teach mathematics: Analysis of a curriculum designed and implemented. Paper presented at the annual meeting of the American Society for Engineering Education, Pittsburgh, PA, USA. 
  • Silk, E. M., Schunn, C. D., Higashi, R., Shoop, R., Dietrich, A., & Reed, R. (2007).The use of robotics to teach mathematics. Robotics Educators Conference, Butler, PA, USA.