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Direct linkSuwannakhan, Athikhun; Casanova-Martínez, Daniel; Yurasakpong, Laphatrada; Montriwat, Punchalee; Meemon, Krai; Limpanuparb, Taweetham – Anatomical Sciences Education, 2020
Forty anatomy articles were sampled from English Wikipedia and assessed quantitatively and qualitatively. Quantitatively, each article's edit history was analyzed by Wikipedia X-tools, references and media were counted manually, and two readability indices were used to evaluate article readability. This analysis revealed that each article was…
Descriptors: Encyclopedias, Collaborative Writing, Electronic Publishing, Anatomy
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Download full textOzasa, Toshiaki; Weir, George R. S.; Fukui, Masayasu – Themes in Science and Technology Education, 2010
We have been engaged in developing a readability index and its application software attuned for Japanese EFL learners. The index program, Ozasa-Fukui Year Level Program, Ver. 1.0, was used in developing the readability metric Ozasa-Fukui Year Level Index but tended to assume a high level of computer knowledge in its users. As a result, the…
Descriptors: Foreign Countries, English (Second Language), Computer Software, Readability
