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Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics

Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
Renu Balyan; Danielle S. McNamara; Scott A. Crossley; William Brown; Andrew J. Karter; Dean Schillinger – Grantee Submission, 2022
Online patient portals that facilitate communication between patient and provider can improve patients' medication adherence and health outcomes. The effectiveness of such web-based communication measures can be influenced by the health literacy (HL) of a patient. In the context of diabetes, low HL is associated with severe hypoglycemia and high…
Descriptors: Computational Linguistics, Patients, Physicians, Information Security
Nahatame, Shingo – Language Learning, 2021
Although text readability has traditionally been measured based on simple linguistic features, recent studies have employed natural language processing techniques to develop new readability formulas that better represent theoretical accounts of reading processes. This study evaluated the construct validity of different readability formulas,…
Descriptors: Readability, Natural Language Processing, Readability Formulas, Reading Processes
Goh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning
Schillinger, Dean; Balyan, Renu; Crossley, Scott A.; McNamara, Danielle S.; Liu, Jennifer Y.; Karter, Andrew J. – Grantee Submission, 2020
Objective: To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. Data Source: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal.…
Descriptors: Literacy, Profiles, Computational Linguistics, Syntax
Crossley, Scott A.; Skalicky, Stephen; Dascalu, Mihai; McNamara, Danielle S.; Kyle, Kristopher – Discourse Processes: A multidisciplinary journal, 2017
Research has identified a number of linguistic features that influence the reading comprehension of young readers; yet, less is known about whether and how these findings extend to adult readers. This study examines text comprehension, processing, and familiarity judgment provided by adult readers using a number of different approaches (i.e.,…
Descriptors: Reading Processes, Reading Comprehension, Readability, Adults
Zagal, Jose P.; Tomuro, Noriko; Shepitsen, Andriy – Simulation & Gaming, 2012
Natural language processing (NLP) is a field of computer science and linguistics devoted to creating computer systems that use human (natural) language as input and/or output. The authors propose that NLP can also be used for game studies research. In this article, the authors provide an overview of NLP and describe some research possibilities…
Descriptors: Literature Reviews, Research Tools, Computational Linguistics, Research Needs