A Sustainable Way to Teach Data Mining and Mapping: Proof of Concept For a Flipped Computational Skill Instruction Module

Cohort: 2017
Fellow: Lars Hinrichs

This project is directed at students with no prior knowledge of computer programming languages. When introducing complex and new skills such as computer programming in a classroom, teachers are often confronted with a lot of worried students and great numbers of very different questions, especially in humanities departments. The lecture setting is not well suited to address the needs of individual students. I propose to develop a flipped module that teaches (1) social media data harvesting, (2) text mining of the harvested data, (3) making digital maps of certain aspects of the data, and (4) presenting each map in an appropriate context, whether simply as a static illustration or as an interactive app on a server. For example: students will be able to collect Twitter posts in real time, obtain the geocoordinates (latitude/longitude) of the location where each post was created, and extract information such as the spatial distribution of certain emerging slang words/constructions, and finally make a map showing the distribution of a phenomenon across space; such a map may be designed with interactive features and presented as a shiny app.