TUES: EAGER: Scaffolding Big Data for Authentic Learning of Computing

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The continued rapid evolution of computation and its impact on the world creates new challenges for the design of learning experiences in computation at the university level. Deeply informed skills and knowledge about computation are increasingly needed in all fields of study, including emerging fields like the "digital humanities." Growing awareness of "computational thinking" as a 21st century competency requires that learning the basics of computation becomes a part of every university student's education. However, weaving together the curriculum, pedagogy, and tools that engage learners with different dispositions and expectations about their learning of computation is a critical challenge. This exploratory project will investigate an approach to meeting this challenge by crafting authentic and engaging learning experiences using "big data" that are about real phenomenon and are realistic in scale and complexity. This approach stimulates motivation to learn and places real world problems at the core of the learning experience. The real world focus encourages additional study of computation and is believed to be especially useful to recruit and retain women in computing-intensive fields.The project will create curriculum and technologies for use in a computational thinking course and two introductory computer science courses. The work will (1) develop novel curriculum resources leveraging "big data", including interactive learning materials integrating graphical programming, visualization, and in-line execution; (2) expand and enhance a framework used to scaffold access to big data streams by adding many new streams and including interfaces to visualization and other critical services; and (3) develop, apply, and analyze an extensive set of assessment measures including assessments related to achievement of learning objectives, student motivation, and the dynamics of student cohorts. This work can serve as a national test-bed that: (1) offers a model for other universities grappling with the challenge of providing education in computation for all students, (2) provides an on-ramp for developing minor courses of study in computer science, (3) creates resources that infuse existing computer science courses with engaging big data projects, and (4) contributes to evolving discussions of theories on how students are best introduced to the computer science.