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A readability level prediction tool for K-12 books

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  • Joel Denning
  • Maria Soledad Pera
  • Yiu-Kai Ng

Abstract

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Suggested Citation

  • Joel Denning & Maria Soledad Pera & Yiu-Kai Ng, 2016. "A readability level prediction tool for K-12 books," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 550-565, March.
    • Handle: RePEc:bla:jinfst:v:67:y:2016:i:3:p:550-565
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    File URL: http://hdl.handle.net/10.1002/asi.23417
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    Cited by:

    1. Ion Madrazo Azpiazu & Maria Soledad Pera, 2020. "Is cross‐lingual readability assessment possible?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(6), pages 644-656, June.
    2. Malgorzata Solarz & Jacek Adamek, 2021. "Factors Affecting Mobile Banking Adoption in Poland: An Empirical Study," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1018-1046.
    3. David M. Goldberg & Nohel Zaman & Arin Brahma & Mariano Aloiso, 2022. "Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(3), pages 419-437, March.
    4. Linling Fu & Lei Liu, 2024. "What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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