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Liuying Gong; Jingyuan Chen; Fei Wu – IEEE Transactions on Learning Technologies, 2025
The capabilities of large language models (LLMs) in language comprehension, conversational interaction, and content generation have led to their widespread adoption across various educational stages and contexts. Given the fundamental role of education, concerns are rising about whether LLMs can serve as competent teachers. To address the…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Comparative Analysis
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Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
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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
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Yuchen Wang; Juxiang Zhou; Zijie Li; Shu Zhang; Xiaoyu Han – IEEE Transactions on Learning Technologies, 2024
Graded reading is one of the important ways of English learning. How to automatically judge and grade the difficulty of the English reading corpus is of great significance for precision teaching and personalized learning. However, the current rule-based readability assessment methods have some limitations, such as low efficiency and poor accuracy.…
Descriptors: Computational Linguistics, Reading Materials, Readability, Semantics
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Chen, Mei-Hua; Huang, Chung-Chi; Huang, Shih-Ting; Chang, Jason S.; Liou, Hsien-Chin – IEEE Transactions on Learning Technologies, 2014
Formulaic language is important to language acquisition; however, English language learners are often reported to have problems with formulaic expressions. Several lists of formulaic sequences have been proposed, mainly for developing teaching and testing materials. However, their limited numbers and insufficient usage information seem unable to…
Descriptors: Brain Hemisphere Functions, Chinese, College Freshmen, Language Usage