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Nga Than; Leanne Fan; Tina Law; Laura K. Nelson; Leslie McCall – Sociological Methods & Research, 2025
Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using…
Descriptors: Artificial Intelligence, Coding, Qualitative Research, Cues
Oscar Stuhler; Cat Dang Ton; Etienne Ollion – Sociological Methods & Research, 2025
Generative AI (GenAI) is quickly becoming a valuable tool for sociological research. Already, sociologists employ GenAI for tasks like classifying text and simulating human agents. We point to another major use case: the extraction of structured information from unstructured text. Information Extraction (IE) is an established branch of Natural…
Descriptors: Artificial Intelligence, Sociology, Social Science Research, Natural Language Processing
Bulut, Okan; Yildirim-Erbasli, Seyma Nur – International Journal of Assessment Tools in Education, 2022
Reading comprehension is one of the essential skills for students as they make a transition from learning to read to reading to learn. Over the last decade, the increased use of digital learning materials for promoting literacy skills (e.g., oral fluency and reading comprehension) in K-12 classrooms has been a boon for teachers. However, instant…
Descriptors: Reading Comprehension, Natural Language Processing, Artificial Intelligence, Automation
Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
Yishen Song; Qianta Zhu; Huaibo Wang; Qinhua Zheng – IEEE Transactions on Learning Technologies, 2024
Manually scoring and revising student essays has long been a time-consuming task for educators. With the rise of natural language processing techniques, automated essay scoring (AES) and automated essay revising (AER) have emerged to alleviate this burden. However, current AES and AER models require large amounts of training data and lack…
Descriptors: Scoring, Essays, Writing Evaluation, Computer Software
Haerim Hwang; Hyunwoo Kim – Language Testing, 2024
Given the lack of computational tools available for assessing second language (L2) production in Korean, this study introduces a novel automated tool called the Korean Syntactic Complexity Analyzer (KOSCA) for measuring syntactic complexity in L2 Korean production. As an open-source graphic user interface (GUI) developed in Python, KOSCA provides…
Descriptors: Korean, Natural Language Processing, Syntax, Computer Graphics
Westera, Wim; Prada, Rui; Mascarenhas, Samuel; Santos, Pedro A.; Dias, João; Guimarães, Manuel; Georgiadis, Konstantinos; Nyamsuren, Enkhbold; Bahreini, Kiavash; Yumak, Zerrin; Christyowidiasmoro, Chris; Dascalu, Mihai; Gutu-Robu, Gabriel; Ruseti, Stefan – Education and Information Technologies, 2020
This article provides a comprehensive overview of artificial intelligence (AI) for serious games. Reporting about the work of a European flagship project on serious game technologies, it presents a set of advanced game AI components that enable pedagogical affordances and that can be easily reused across a wide diversity of game engines and game…
Descriptors: Artificial Intelligence, Educational Games, Educational Technology, Computer Software
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Charlton, Patricia; Magoulas, George; Laurillard, Diana – Technology, Pedagogy and Education, 2012
The paper advocates an approach to learning design that considers it as creating digital artefacts that can be extended, modified and used for different purposes. This is realised through an "act becoming artefact" cycle, where users' actions in the authors' software environment, named Learning Designer, are automatically interpreted on…
Descriptors: Foreign Countries, Educational Technology, Instructional Design, Computer Software
Gasevic, D.; Zouaq, Amal; Torniai, Carlo; Jovanovic, J.; Hatala, Marek – IEEE Transactions on Learning Technologies, 2011
Recent research in learning technologies has demonstrated many promising contributions from the use of ontologies and semantic web technologies for the development of advanced learning environments. In spite of those benefits, ontology development and maintenance remain the key research challenges to be solved before ontology-enhanced learning…
Descriptors: Foreign Countries, Natural Language Processing, Computer Interfaces, Web 2.0 Technologies

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