A Generic Framework for Engaging Online Data Sources in Introductory Programming Courses
This paper presents work on a code framework and methodology to facilitate the introduction of large, real-time, online data sources into introductory (as well as advanced) Computer Science courses. The framework is generic in the sense that (in most instances) no prior scaffolding or template specification is needed to make the data accessible, as long as the source uses a standard format such as XML, CSV, or JSON. The implementation described here aims to maintain minimal syntactic overhead while relieving novice programmers from low-level issues of parsing raw data from a web-based data source. At the same time, it interfaces directly with data structures and representations defined by the students themselves, rather than predefined and supplied by the library. Together, these features allow both students and instructors to focus on algorithmic aspects of processing a wide variety of live and large data sources, without having to deal with low-level connection, parsing, extraction, and data binding issues. The library is available at http://cs.berry.edu/big-data and has been used by the author in an introductory programming course based on Processing (i.e. Java).
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