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A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning
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
Michelle L. Rizzella; Edward J. O'Brien – Discourse Processes: A Multidisciplinary Journal, 2024
We examined the impact of prospective information on the processing of information occurring within the present timeline of narrative stories. Participants read target sentences that were consistent with events occurring within a protagonist's present timeline but inconsistent with events in the protagonist's future. When prospective information…
Descriptors: Information Literacy, Information Processing, Sentences, Memory
Tsubasa Minematsu; Atsushi Shimada – International Association for Development of the Information Society, 2024
In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…
Descriptors: Educational Technology, Technology Uses in Education, Error Patterns, Sentences
Tal Ness; Valerie J. Langlois; Albert E. Kim; Jared M. Novick – Perspectives on Psychological Science, 2025
Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., "The cat was chased by the mouse"). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays…
Descriptors: Cognitive Ability, Language Processing, Psycholinguistics, Cues
Byung-Doh Oh – ProQuest LLC, 2024
Decades of psycholinguistics research have shown that human sentence processing is highly incremental and predictive. This has provided evidence for expectation-based theories of sentence processing, which posit that the processing difficulty of linguistic material is modulated by its probability in context. However, these theories do not make…
Descriptors: Language Processing, Computational Linguistics, Artificial Intelligence, Computer Software
Joseph C. Y. Lau; Emily Landau; Qingcheng Zeng; Ruichun Zhang; Stephanie Crawford; Rob Voigt; Molly Losh – Autism: The International Journal of Research and Practice, 2025
Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor…
Descriptors: Artificial Intelligence, Models, Pragmatics, Language Variation
Elena I. Shpit; Philip M. McCarthy – Language Teaching Research, 2025
Virtually all researchers understand the requirement of presenting their studies in peer-reviewed English-medium journals. Russian scientific writers understand this necessity too; however, evidence suggests that these particular researchers are under-performing relative to similar non-native English speakers. The considerable challenge Russians…
Descriptors: Russian, Engineering Education, Researchers, English (Second Language)