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Hei-Chia Wang; Yu-Hung Chiang; I-Fan Chen – Education and Information Technologies, 2024
Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not easy for students to obtain question banks. To solve…
Descriptors: Natural Language Processing, Test Construction, Test Items, Models
Mengjiao Zhang – ProQuest LLC, 2024
The rise of Artificial Intelligence technology has raised concerns about the potential compromise of privacy due to the handling of personal data. Private AI prevents cybercrimes and falsehoods and protects human freedom and trust. While Federated Learning offers a solution by model training across decentralized devices or servers, thereby…
Descriptors: Privacy, Cooperative Learning, Natural Language Processing, Learning Processes
Annamaria V. Wolf – ProQuest LLC, 2023
Peer Evaluation Systems (PESs) allow members of student teams to provide one another with computer-mediated feedback in the form of qualitative, open-ended comments. The current research leverages unsupervised Natural Language Processing (NLP), namely Biterm Topic Modeling (BTM) and sentiment analysis, to uncover latent topics and degree of…
Descriptors: Students, Natural Language Processing, Peer Evaluation, Feedback (Response)
Laura K. Allen; Sarah C. Creer; Püren Öncel – Grantee Submission, 2022
As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students' responses to a variety of assignments and tasks. While NLP…
Descriptors: Natural Language Processing, Learning Analytics, Learning Processes, Methods
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Carvalho, Floran; Henriet, Julien; Greffier, Francoise; Betbeder, Marie-Laure; Leon-Henri, Dana – Journal of Education and e-Learning Research, 2023
This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this…
Descriptors: Learning Processes, Algorithms, Artificial Intelligence, Programming Languages
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Behzad Mirzababaei; Viktoria Pammer-Schindler – IEEE Transactions on Learning Technologies, 2024
In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure,…
Descriptors: Learning Processes, Electronic Learning, Artificial Intelligence, Natural Language Processing
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Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
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Sebastian Hobert; Florian Berens – Educational Technology Research and Development, 2024
Individualized learning support is an essential part of formal educational learning processes. However, in typical large-scale educational settings, resource constraints result in limited interaction among students, teaching assistants, and lecturers. Due to this, learning success in those settings may suffer. Inspired by current technological…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Learning Processes, Teaching Methods
Ryan Daniel Budnick – ProQuest LLC, 2023
The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic…
Descriptors: Grammar, Feedback (Response), Algorithms, Computational Linguistics
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Torres-Jimenez, Jose; Lescano, Germán; Lara-Alvarez, Carlos; Mitre-Hernandez, Hugo – Education and Information Technologies, 2023
Conflicts play an important role to improve group learning effectiveness; they can be decreased, increased, or ignored. Given the sequence of messages of a collaborative group, we are interested in recognizing conflicts (detecting whether a conflict exists or not). This is not an easy task because of different types of natural language…
Descriptors: Conflict, Identification, Computer Assisted Instruction, Cooperative Learning
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Unger, Layla; Yim, Hyungwook; Savic, Olivera; Dennis, Simon; Sloutsky, Vladimir M. – Developmental Science, 2023
Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of…
Descriptors: Natural Language Processing, Language Usage, Vocabulary Development, Linguistic Input
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Gyeonggeon Lee; Xiaoming Zhai – TechTrends: Linking Research and Practice to Improve Learning, 2025
Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…
Descriptors: Artificial Intelligence, Computer Software, Teaching Methods, Learning Processes
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Aras Bozkurt; Ramesh C. Sharma – Asian Journal of Distance Education, 2024
This study explores the transformative potential of Generative AI (GenAI) and ChatBots in educational interaction, communication, and the broader implications of human-GenAI collaboration. By examining the related literature through data mining and analytical methods, the paper identifies three main research themes: the revolutionary role of…
Descriptors: Algorithms, Artificial Intelligence, Man Machine Systems, Technology Uses in Education
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Williams, John N. – Language Learning, 2020
Over the past decades, research employing artificial grammar, sequence learning, and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little…
Descriptors: Learning Processes, Grammar, Sequential Learning, Natural Language Processing
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William Cain – TechTrends: Linking Research and Practice to Improve Learning, 2024
This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Prompting
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