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Direct linkBalyan, 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 textBalyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 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
Dahl, Amanda C.; Carlson, Sarah E.; Renken, Maggie; McCarthy, Kathryn S.; Reynolds, Erin – Language, Speech, and Hearing Services in Schools, 2021
Purpose: Complex features of science texts present idiosyncratic challenges for middle grade readers, especially in a post-Common Core educational world where students' learning is dependent on understanding informational text. The primary aim of this study was to explore how middle school readers process science texts and whether such…
Descriptors: Science Materials, Textbooks, Difficulty Level, Readability
