Carnegie Mellon University

Carnegie Mellon Robotics Academy

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

Building a Theory of Badges for Computer Science Education

Project Description

There is a pressing need for improvement and expansion of Computer Science education in the US. 21st Century. Industry will demand computational literacy from all workers, with an increasing shift of high-wage positions toward computational occupations. Computer Science Education needs a means to accelerate its adoption without loss of focus on the key Computer Science Principles identified by the College Board and NSF. Further, Computer Science Education needs to address issues of gender and ethnicity diversity, leveraging new methods for motivating broad participation in computer science. We, therefore, build on the current success of competitive robotics for attracting a broader set of learners and seek to deepen their understanding and interest in CS.

Badges – simple, visually prominent, validated indicators of performance – have recently attracted a great deal of attention as a tool to motivate students and mark significant learning accomplishments. The badge’s dual role as both motivator and assessment marker raises interesting questions about the ability of existing theories of Motivation and theories of Assessment to fully predict, explain, and guide the design of badges for student motivation AND assessment. Carnegie Mellon University and the University of Pittsburgh’s joint project will test and refine a Theory of Badges applied to Computer Science Education, in which we divide badges into one of three categories, each reflecting a specific set of motivational and performance-shaping assessment mechanics which current Motivation and Assessment research predict will affect student performance.

We propose to test and refine this theory by investigating and experimentally manipulating the use of badges within an ongoing Computer Science education development project, the Computer Science Student Network (CS2N). CS2N contains a badge system that maps well to the three-category system proposed by our theory. We will monitor and adapt the form and content of CS2N assessments and badge representations in CS content modules to try to achieve the best possible outcomes for student participants as predicted by the current iteration of our badge theory. These modules map to different Learning Objectives identified by The College Board’s Computer Science Principles.

Intellectual Merit

We propose to develop a Theory of Badges for Computer Science Education, and in so doing, answer the following Design, Research, and Evaluation questions:

• Design: Which particular badges: Are perceived as desirable, easily understood by students, and are accurate indicators of performance?
• Research: Does our Badge Theory predict associations of particular Badges with particular motivational states?
• Research: Does our Badge Theory predict Pathways of motivational variables to larger outcomes (skills and career goals)?
• Evaluation: Does the overall badge ecosystem increase: learner persistence, CS content learning, and CS career interest?

Broader Impacts

Building on a strong partnership with very large VEX and FTC robotics competitions, the Computer Science Student Network will serve thousands of student and teacher users over the course of this study; improvements to its badge system design will directly benefit them. It can also be leveraged as a scaling platform to reach many more students in the future, and deliver additional badged CS content. Improvements to content module assessments will help to align them with the CS Principles. The Theory of Badges will help to guide future development of both CS-related badges, and of badge systems in general. CS2N can be made available as a research platform for future research in CS Education. Research results on both badges and CS learning will be published. Special attention will be given to the effects of badges on underrepresented learner populations. Some content modules feature technologies believed to particularly benefit low-resource learning environments.