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A Text Mining Based Literature Analysis for Learning Theories and Computer Science Education

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

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Abstract

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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Author information

Authors and Affiliations

  1. Columbus State University, Columbus, GA, 31907, USA

    Rania Hodhod & Hillary Fleenor

Authors
  1. Rania Hodhod
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  2. Hillary Fleenor
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Corresponding author

Correspondence to Rania Hodhod .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Hodhod, R., Fleenor, H. (2018). A Text Mining Based Literature Analysis for Learning Theories and Computer Science Education. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_52

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

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