Vu Nguyen

Building a Theory of Badges for Computer Science Education

CMU and the University of Pittsburgh collaborated to test a Theory of Badges in Computer Science Education. This theory categorized badges, which are visual indicators of achievement, into three groups, each with a unique motivational and assessment function. Within the Computer Science Student Network (CS2N), they adjusted badge displays and assessments to optimize student outcomes, aligning with Learning Objectives from The College Board’s Computer Science Principles.

Smart Manufacturing and Advanced Robotics Training (SMART) Program Micro-Credential Courses

Developed:
-Electrical Foundations
-Mechanical Foundations
-Fabrication Foundations
-Software Foundations
-Robotics Integration
-Applied AI and Robotics
-Autonomy Foundations
-Robot Operations

PPG Artificial Intelligence Pathways Institute (AIPI)

The PPG AIPI program is a partnership with the Boys and Girls Clubs of Western PA that provided students (aged 14-17) with a three-week introduction to artificial intelligence, human-centered design, and STEM careers. Contributions to this program included identifying the Knowledge, Skills, and Attitudes relevant to the industry, developing pre-post tests for collecting knowledge gains data, and participating in the final presentation evaluations panel.

University of Pittsburgh

B.S.E.

Computer Engineering

Higashi, Nguyen & Flot, Resulting Knowledge, Skills, and Attitudes list from SMART Project

Higashi, Nguyen & Flot

This list is the result of a research project funded by the ARM Institute, called the Smart Manufacturing and Advanced Robotics Training (SMART) Project. SMART consists of five micro certification courses that focus on the Knowledge, Skills, and Attitudes (KSA) that are needed for Robotics Technicians in the Advanced Manufacturing and Robotics industry. The KSA was identified through research which involved interviewing and observing Robotics Technicians in the industry to ask questions like:

● What skills do you need to be successful in your position?
● What tools do you use most frequently in your work?
● What does your day-to-day look like?
● What kind of teammate would you like to have working alongside you?

Hiring managers were also interviewed to understand what type of workers they look for to join their company. The SMART program consists of five micro-certification courses (estimated 40 hours each, 200 hours total):

1. Mechanical Foundations
2. Electrical Foundations
3. Fabrication Foundations
4. Software Foundations
5. Robotics Integration

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Results of National Association of Manufacturers Member Interviews around ML and AI

Dr. Ross Higashi, Vu Nguyen, Jesse Flot

2020-09-23

As automation technologies, sensor hardware, network connectivity, and computing power become more accessible to companies, the manufacturing industry is preparing for change. Machine Learning (ML) and Artificial Intelligence (AI) have enormous potential to turn
huge pools of accumulated sensor and operational data into useful insight at all levels, from business intelligence to equipment configuration. Mass network connectivity allows this information to be collected from ubiquitous sensors and acted upon in real time. Access to computing power in the cloud makes processing this data both possible and affordable. Robotics and control automation technologies close the loop, allowing these informational insights to translate back into coordinated physical action on the factory floor. To emphasize the extent of such a change, proponents of this vision liken it to a fourth industrial revolution: Industry 4.0.

This framing puts technology in the spotlight, and indeed technology is both enabling and driving the change. In practice, however, the transition from “Industry 3.0” to “Industry 4.0” is heavily dependent upon the skills of the workforce. What knowledge and skills do workers need to see this transition through? What skills will allow them to thrive both during and after it? Who needs which skills? These answers are contingent, of course, on the paths companies have charted for themselves toward an AI future. But if a sectoral “Industry 4.0” transformation is under way in the United States, it has been far from overnight. Where are manufacturers really at in their adoption of ML and AI technologies? What are their goals? What obstacles are standing in their way? And how can the American workforce be best prepared to help both companies and workers succeed?

This paper shares the results of Carnegie Mellon Robotics Academy’s (CMRA) interviews with six companies that range from AI technology vendors to manufacturers. The interviews were conducted as part of an NSF Convergence Accelerator-funded research project called Rapid Dissemination of AI Microcredentials through Hands-on Industrial Robotics Education (RDAIM-HIRE) to design and scale new methods of training to meet the technical workforce demands of the near future, especially where current systems were not working well.

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Coordinating Evidence Across Learning Modules using Digital Badges

Higashi, R., Schunn, C., Nguyen V., & Ososky, S.

2017-08-13

No matter how successful a learning module or intervention such as an intelligent tutoring system (ITS) is at producing learning, the fruits of those efforts cannot be employed efficiently without a suitable means for representing and conveying which learners possess which skills. Who will know to hire or promote this more knowledgeable individual, if there is no clear sign that they are more accomplished? Digital badges are digital artifacts that function as markers of achievement. Often described as building on the combined traditions found within Scouting (e.g., Boy Scouts or Girl Scouts) and online gaming (e.g., Xbox Live Achievements, PlayStation Network Trophies), badges are issued to an individual when the individual meets specific criteria embedded in program-relevant activities (Ostashewski & Reid, 2015).

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