Talk on the CORGIS Big-Data Framework and the MUSIC model for assessing student motivation
As Computer Science expands to more non-CS majors under the banner of Computational Thinking, it is important that these students perceive the experience as useful to them. Authentic and motivating projects is one way to engage students. To that end, big data - crucial in fields from agriculture to history to science and beyond - offers a promising educational option. However, teaching big data topics to non-CS majors is a challenging undertaking, strewn with technological obstacles such as the unavailability of data, irregular structure, and difficulty of dissemination. To solve the technical difficulties, we introduce a new project: CORGIS - a “Collection of Real-time, Giant, Interesting, Situated Datasets” which encompasses two distinct goals: (1) create a new context for introductory programming, and (2) introduce new content related to big data. CORGIS accommodates both of these goals by means of technological scaffolding. The CORGIS project comprises a collection of libraries that provide an interface to big data for students, and architectures for rapidly enabling new high velocity and high volume data sources.
In this talk, we give an overview of CORGIS’ capabilities, review a model (MUSIC) others have developed for assessing student motivation, and discuss how we might apply MUSIC to assess CORGIS.
Tue 21 Oct
|08:30 - 09:00|
|09:00 - 09:30|
Austin Cory Bart, Jason Riddle, Omar Saleem, Bushra Chowdhury, Eli Tilevich, Cliff Shaffer, Dennis KafuraFile Attached
|09:30 - 10:00|