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Collaborative Augmentation and Simplification of Text (CoAST): Pedagogical Applications of Natural Language Processing in Digital Learning Environments

Title: Collaborative Augmentation and Simplification of Text (CoAST): Pedagogical Applications of Natural Language Processing in Digital Learning Environments
Language: English
Authors: Shardlow, Matthew; Sellar, Sam; Rousell, David (ORCID 0000-0002-4509-6128)
Source: Learning Environments Research. Jul 2022 25(2):399-421.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2022
Document Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Descriptors: Natural Language Processing; Educational Environment; Educational Technology; College Students; Computer Software; Learner Engagement; Technology Uses in Education; Reading Ability; Artificial Intelligence
DOI: 10.1007/s10984-021-09368-9
ISSN: 1387-1579
Abstract: The digitisation of higher education is raising significant questions about the impact of artificial intelligence and automation on teaching and learning environments, highlighting the need to investigate how teachers and students can work with new educational technologies in complementary ways. This paper reports results from a pilot study of the collaborative augmentation and simplification of text (CoAST) system, which is online software designed to facilitate the engagement of university students with theoretically-sophisticated academic texts. CoAST offers a digital learning interface that uses natural language processing algorithms to identify words that can be difficult to understand for readers at different ability levels. Course lecturers use their pedagogical content knowledge to add brief annotations to identified words. The software was trialed using a quasi-experimental design with: (1) 23 undergraduate Education Studies students; and (2) 23 digital and technology solutions students. Results suggest that CoAST offers a digital learning environment that can effectively mediate and enhance pedagogical relationships between teachers, students, and complex theoretical texts.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1338322
Database: ERIC