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Hannah K. D’Apice; Patricia Bromley – Environmental Education Research, 2023
Anthropogenic climate change is a scientific fact, but U.S. public discourse around the issue remains mired in controversy, including in education. Our study leverages natural language processing methods to give a precise look into the extent to which climate change-related topics are covered in 30 of the most widely used high school history…
Descriptors: Environmental Education, Climate, Discourse Analysis, United States History
Nonkanyiso Pamella Shabalala – Research in Social Sciences and Technology, 2024
The integration of Artificial Intelligence (AI) into Open Distance eLearning (ODeL) represents a significant evolution in STEM education, offering transformative benefits in teaching, learning and administrative processes. This conceptual paper explores how AI-driven platforms are revolutionising ODeL by providing personalised learning…
Descriptors: STEM Education, Distance Education, Artificial Intelligence, Educational Technology
Alexander Johnson – ProQuest LLC, 2024
The potential of speech technology to improve educational outcomes has been a topic of great interest in recent years. For example, automatic speech recognition (ASR) systems could be employed to provide kindergarten-aged children with real-time feedback on their literacy and pronunciation as they practice reading aloud. Within these systems,…
Descriptors: Audio Equipment, Black Dialects, African American Students, Equal Education
Stefan Ruseti; Mihai Dascalu; Amy M. Johnson; Renu Balyan; Kristopher J. Kopp; Danielle S. McNamara – Grantee Submission, 2018
This study assesses the extent to which machine learning techniques can be used to predict question quality. An algorithm based on textual complexity indices was previously developed to assess question quality to provide feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). In…
Descriptors: Questioning Techniques, Artificial Intelligence, Networks, Classification
Balyan, Renu; Crossley, Scott A.; Brown, William, III; Karter, Andrew J.; McNamara, Danielle S.; Liu, Jennifer Y.; Lyles, Courtney R.; Schillinger, Dean – Grantee Submission, 2019
Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate…
Descriptors: Patients, Literacy, Health Services, Profiles
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

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