CyberTraining: DSE—The Code Maker: Computational Thinking for Engineers with Interactive, Contextual Learning

----- AWARDED July 2017 --------

This project will develop “The Code Maker,” a training program for engineering undergraduates that embeds computing in the curriculum with interactive, context-based learning modules. It will deliver 8 or more modules, each consisting of a series of four or more lessons, written as a Jupyter Notebook. The modules will be available online and can be completed asynchronously, or assigned as a graded course component. Success in the modules will be a requirement for completion, i.e., they adopt a mastery-learning approach. The training program will be supported by a team of learning assistants and a program of maker-inspired events at a newly created space in the Library, the “STEM Lab.” It will use cloud infrastructure, both public and private: an instance of Open edX on AWS that effectively allows running the program publicly as a MOOC; and a local JupyterHub server to eliminate installation friction and ensure a consistent compute environment for local students. The evaluation will apply a combination of 4-level training evaluation and a Technology Acceptance model. Student performance will be tracked in courses that use computing and additional qualitative assessment will be done with think-aloud interviews. All materials will be openly developed on the web and shared under permissive licenses, allowing reuse and derivative works. The project has secured three external collaborators that commit to peer review, modify for local conditions and/or trial the modules. Informal advisors from large industry (e.g., Boeing, GE Global) will counsel on authentic, industry relevant computing skills.

Keywords: contextual learning, computational thinking, just-in-time teaching, interactive computing, blended learning

Cognizant Program Officer Consulted: Sushil K. Prasad, CISE/ACI

Contextual learning of computing is an evidence-based approach (e.g., media computing courses). PI Barba has previously published computational modules that have been used in the classroom effectively: CFD Python and AeroPython. She also led a MOOC based on Jupyter Notebooks: “Practical Numerical Methods with Python,” which surpasses 7,000 people registered. The design of the modules will be supported by co-investigator Ryan Watkins who is an expert in e-learning and assessment. Co-PI Wickenheiser (robotics expert) will lead a maker-inspired program of events at the STEM Lab. The evaluation will apply the Technology Acceptance model: acceptance of a technological innovation is determined by judgements on perceived usefulness and perceived ease of use. Application of this model to study the acceptance of educational technology by both students and faculty is well documented.

This project will train computationally skilled engineers who are prepared to enter the workforce competitively, and ready to use computing effectively as a research tool if joining a PhD program in computational science and engineering. They will develop confidence and computational literacy and be ready to become cyberinfrastructure users in both industry or research. The material will be built around core engineering content so that the program may be adopted fully or in part at other universities. External collaborators will trial the program at other institutions and help build a community around the materials and pedagogical methods of this project. Community building efforts online will gain from previous efforts of the PI, including an independent MOOC that attracted more than 7,000 users. The impact will be reinforced from applying the collaborative ethos of open source. Using open-source tools and teaching learners about the open-source world has the added value of showing effective coordination of teams via online platforms. Open-source projects are able to create value from collective work thanks to a culture of commitment and transparency. Our training will emulate this culture, building an online community and enticing local students with activities at the STEM lab. The local engineering student cohort is 40% female, and we commit to a target of 50% female learning assistants.