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• W4387029274 startingPage "Reg" @default.
• W4387029274 abstract "In order to assess the extent to which text-mining techniques can be used to gain insights into a particular topic area, we apply hierarchical word clustering and the Term Frequency-Inverse Document Frequency (TF-IDF) measure to articles on computer programming published since the 1970s, when research articles on teaching programming are now more readily available in PDF files. Study 1 compares two sets of papers published before and after the introduction of the concept of Computational Thinking in 2006 to highlight the changes seen in these research sets. Articles mentioned in frequently cited review papers were selected as the target articles to ensure the quality of the sample. Study 2 extends the sample pool to include a range of papers published after the 1970s, allowing us to examine the stability of the conceptual structures identified in Study 1. In both studies, the obtained word clusters or concepts align with known research trends in the programming-education literature. The significance and potential of text-mining techniques are also discussed." @default.
• W4387029274 created "2023-09-26" @default.
• W4387029274 creator A5033628539 @default.
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• W4387029274 date "2023-01-01" @default.
• W4387029274 modified "2023-10-02" @default.
• W4387029274 title "Text Mining Analyses of Programming Education Articles Since the 1970s" @default.
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• W4387029274 doi "https://doi.org/10.12937/itel.3.1.reg.p001" @default.
• W4387029274 hasPublicationYear "2023" @default.
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