A text mining based literature analysis for learning theories and computer science education

Document Type

Conference Proceeding

Publication Date


Publication Title

Advances in Intelligent Systems and Computing



First Page


Last Page



Computer science education, Learning theories, Teaching data structures, Text mining


© 2018, Springer International Publishing AG. Text mining has been successfully used to discover interesting patterns and extract useful information from analyzing massive text data exists on the internet, books and other text sources. Computer science education has become an initiative for The National Science Foundation (NSF) and the White House Office of Science and Technology Policy (OSTP) in the United States. Finding the right tools and technologies that can support that initiative and help students succeed and do well while studying computer science is a plus. Although the literature is rich with research for computer science education, there is no clear guide on the use of learning theories to design educational games to teach computer science. Text mining can analyze the literature to find trends for designing educational games for computer science education, in addition to identifying existing gaps. The paper presents the results from analyzing 204 papers to discover the current state of research related to computer science education, using Voyant, a text mining tool. Analysis of results should provide an insight on how learning theories have been considered/used in computer science education and guide future research through identifying what learning theories need to be considered in designing educational games to teach computer science topics, such as data structures.

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