Carnegie Mellon Robotics Academy
Curriculum Research

This page documents the ongoing research conducted by the Robotics Academy and the University of Pittsburgh’s Learning Research and Development Center.

Ongoing Studies

  1. Teaching Robot Programming Through Simulation
  2. Robots in Motion Robot Algebra Project

Teaching Programming through Robotics

  1. 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. [ PDF ]
  2. Flot, J., Shoop, R (November 2013). Teaching Programming with Robot Virtual Worlds. Presentation given at the Technology Education and Engineering Association of Pennsylvania Annual Conference, Camp Hill Pennsylvania. [ PDF copy of PowerPoint ]
  3. Flot, J., Shoop, R (November 2013). Foregrounding Math, Engineering, and Computer Science using Robotics. Presentation given at the Technology Education and Engineering Association of Pennsylvania Annual Conference, Camp Hill Pennsylvania. [ PDF copy of PowerPoint ]
  4. Liu, A., Newsom, J., Schunn, C., Shoop, R. Learn to program in half the time!. Robot Magazine , 49-51. [ Author Proof (PDF) ]
  5. Soldaat, X., Friez, T., Flot, J. Pointers and Data Structures in ROBOTC. Robot Magazine , 59-61. [ Author Proof (PDF) ]
  6. Liu, A., Newsom, J., Schunn, C., Shoop, R. Students Learn Programming Faster through Robotic Simulation. Tech Directions , 16-19. [ Author Proof (PDF) ]
  7. Flot, J., Lui, A., Schunn, C., Shoop, R. (November 2012). Learning How to Program via Robot Simulation. Robot Magazine , 68-70. [Author Proof (PDF)]
  8. Avanzato, R.,Choset, H., Friez, T., Shoop, R. Veloso, M. (2011, December). Programming and Multi-Robot Communications Robot Magazine , 74-77. [Author Proof (PDF)]
  9. Atwood, T., Shoop, R. Carnegie Mellon Launches a Mega Million Dollar Robotics Education Initiative. Robot Magazine , 70-71. [Author Proof (PDF)]
  10. Shoop, R. (2011, May). FIRE Unveils Robot Virtual World Games Robot Magazine , 78-81. [Author Proof (PDF)]
  11. Shoop, R. (2011, January) Computer Science Student Network Project. Presented at the Computing Education for the 21st Century (CE21) meeting, New Orleans [Handout]
  12. Higashi, R., Shoop, R. (2011, November) Organizational Expectations Presented to Propel School System Teachers and Administrators, Robot Algebra Partnership Kickoff [Handout]

Badges, Motivation, and Assessment

  1. Abramovich, S., Schunn, C.D., Higashi, R. (2012, August) Are Badges Useful in Education?: It Depends Upon the Type of Badge and Type of Learner. Pittsburgh, PA [ Paper PDF ]
  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 [Paper PDF]
  3. Abramovich, S., Higashi, R., Hunkele, T. Schunn, C.D., Shoop, R. (2011, July) Achievement Systems to Boost Achievement Motivation. 2011 GLS Conference [Paper PDF]

Approaches in Teaching Mathematics and Robotics

  1. 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. [Paper (PDF)]
  2. 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.[Paper (PDF)]
  3. 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) [Presentation (PDF)] [Paper (PDF)]
  4. 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. [Paper (PDF)] [Presentation (PDF)]
  5. 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. [Paper (PDF)] [Presentation (PDF)]
  6. 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 meetingof the American Educational Research Association, New Orleans, LA, USA. [Paper (PDF)] [Presentation (PDF)]
  7. 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. [Poster (PDF)]
  8. Silk, E. M. (2010, August 25). Can math help in LEGO robotics competitions? [4-partweb logpost]. Retrieved fromhttp://robotics-academy.org/blog/?p=356[Part 1] [Part 2] [Part 3] [Part 4]
  9. Silk, E. M., Higashi, R., Shoop, R., & Schunn, C. D. (2010). Designing technology activities that teach mathematics.  The Technology Teacher, 69 (4), 21-27. [Paper (PDF)]
  10. 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. [Author Proof (PDF)]
  11. 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 meetingof the American Society for Engineering Education, Pittsburgh, PA, USA. [Paper (PDF)] [Presentation (PDF)]
  12. 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. [Presentation (PDF)]

Other Notable Research

Badge Research

  1. Antin & Churchill, Badges In Social Media: A Social Psychological Perspective – In this short paper Antin and Churchill describe badges from a social scientist's perspective. [PDF]

National Studies around CS-STEM

  1. National Science Board (2010). Preparing the next generation of STEM innovators: Identifying and developing our nation’s human capital. Publication NSB-10-33 of the National Science Foundation. http://www.nsf.gov/nsb/stem/innovators.jsp
  2. Gal-Ezer, J. & Stephenson, C. (2009). The Current State of Computer Science in U.S. High Schools: A Report from Two National Surveys. Journal for Computing Teachers, Spring 2009
  3. CSTA (Computer Science Teachers Association) (2009). CSTA National Secondary Computer Science Survey: Comparison of 2005, 2007, and 2009 Survey Results. http://csta.acm.org/Research/sub/CSTAResearch.html
  4. "Enrollments and Degree Production at US CS Departments Drop further in 2006/2007", CRA Bulletin, March 2008. http://www.cra.org/wp/index.php?p=139
  5. "Fastest Growing Occupations", Monthly Labor Review, November 2007. http://www.bls.gov/emp/emptab21.htm

 

Premise of the research:
Standardized tests such as the PSSA have identified several areas where students are not performing well in school. Carnegie Mellon believes that under performance in some cases is caused by lack of student motivation as well as teachers being under prepared to teach STEM concepts using student relevant examples. Carnegie Mellon believes that student STEM learning can be improved by employing contexts that involve concrete, hands-on-mind-on approaches that are motivated by rich real-world examples, and linked to other disciplines that students are studying. Research has shown that:

  • Motivated and interested students yield a higher rate of completion of tasks.
  • Student’s motivation increases when teachers use current technology to explain STEM concepts rather than traditional examples.
  • Student retention rate is increased through concrete interactions well-suited to their developmental level.
  • Cross-curricular connections enable students to establish a robust global understanding of what they are studying which will enable the new learner to reconstruct knowledge at a later time if parts of the understanding are missing.

Carnegie Mellon has observed that students are intrigued by robots and believes that robots can be used as a motivational tool to teach fundamental STEM. 

The Carnegie Mellon/University of Pittsburgh research will

  • Build on Carnegie Mellon University’s Robotics Academy lessons learned over the last seven years of robotics curricula development.
  • Build on the University of Pittsburgh’s Learning Research and Development Center’s immersion unit concept.
  • Align robotic concepts with 6-8 PA STEM standards, initially mathematics, but eventually cross discipline standard alignment across all relevant subject areas.
  • Provide structured teacher professional development classes designed to improve teacher’s understanding of how to use robotics as a tool to teach fundamental STEM concepts and make those classes available statewide.
  • Provide ongoing support for teachers implementing robotic immersion units in their classrooms by:
    • Providing technical assistance and real-time feedback
    • Creating communities of learners using robotics as an organizer to teach STEM as well as brand Pennsylvania as the place to go for robotics.
    • Identifying the best method of explaining STEM concepts through Robotics
    • Helping teachers to develop assessment tools that align with student/teacher formative and summative assessment needs
    • Providing resources to implement lessons
  • Measure the results of student understanding after teachers teach these immersion units.
  • Iteratively improve teacher professional development and student lessons based on testing.

Sample Assessment Math Anchor Alignment

Sample Abstraction Bridge to Standardized Assessment

Goals of the research

  1. Align robotics instruction to what is being assessed by NCLB.
  2. Improve robotic instruction through an iterative test revise cycle.
  3. Improve teacher professional development using the same cycle.
  4. Identify areas where robotics curriculum can enhance/augment student understanding and replace existing instructional practices in the classroom.
  5. Develop effective formative and summative assessment tools to measure student understanding.
  6. Conduct research to evaluate the relative effectiveness of the Robotics units compared to existing instruction in those areas.
  7. Reach conclusions regarding:
    1. The overall effectiveness of the Robotics units compared to the traditional curriculum
    2. Specific “strength” and “weakness” areas or aspects of the robotics lessons that can be built upon and used to inform future development

Structure of the research

  1. Identify concept areas where students do not perform well in school.
  2. Identify the concepts that can be taught using the motivational effects Robotics.
  3. Analyze the “alignment” of the areas where students do not perform well with the areas covered by the robotics lessons.
  4. Identify regions where robotics lessons could have a maximal impact.
  5. Formulate a fair metric to measure performance in the target areas, drawing on contextual requirements such as subject standards and testing practices
  6. Design an intervention study that can be conducted with students to measure the performance of groups in the standard curriculum, compared to a group that uses the robotics lessons.
  7. In conjunction with administrators and teachers, coordinate a pilot implementation of the study to test and refine robotics immersion units for larger-scale deployment.
  8. Observe teacher implementation of robotics immersion unit.
  9. Analyze preliminary results from the pilot study, and revise methodology as necessary based on issues discovered.
  10. Conduct a large-scale study.
  11. Collect, evaluate, and publish results.

Outcomes of the research

  • Robotics Academy will be able to cite a formal study on the effectiveness of educational robotics in the classroom, and use that feedback to inform future curriculum development efforts
  • Participating school districts will have received STEM specific professional development and support intended to help their teachers teach STEM, but also think about how students learn,
  • LRDC will further its mission of conducting research “on learning and teaching, reform and improvement of schooling, and individual and organizational learning in the workplace… [leveraging] information technologies developed for, or as a result of, its work in all of these areas.”

Survey
Robotics Academy Curriculum Customer Survey
Pitsco Market Research Department
Robotics is having a positive impact on the teaching of science, technology, engineering, and math. One of the respondents indicated that “Students are receiving practical, hands-on knowledge from which they are able to apply knowledge learned in their math and science classes.”