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Sang-Gu Kang – Journal of Pan-Pacific Association of Applied Linguistics, 2024
This paper reports on a young Korean boy's target-like and non-target-like uses of the Korean negation marker "ani" to express various types of negation in Korean, observed approximately between the ages of 2;2 and 2;5. Besides the target-like usage of "ani" as a sentential adverb for a 'no' response, he used "ani" in…
Descriptors: Korean, Morphemes, Toddlers, Language Acquisition
Claudio-Rafael Vasquez-Martinez; Francisco Flores-Cuevas; Felipe-Anastacio Gonzalez-Gonzalez; Luz-Maria Zuniga-Medina; Graciela-Esperanza Giron-Villacis; Irma-Carolina Gonzalez-Sanchez; Joaquin Torres-Mata – Bulgarian Comparative Education Society, 2024
Language is the basis of human communication and is the most important key to complete mental development and thinking. Therefore, children must learn to communicate using appropriate language. For this to happen, the development of language in the child must be understood as a biological process, complete with internal laws and with marked stages…
Descriptors: Infants, Morphology (Languages), Syntax, Phonology
Erin Pacquetet – ProQuest LLC, 2024
This dissertation explores the relationship between language production processes and recorded typing behaviors among native speakers of English writing in their native language. Typing is quite prevalent in modern societies, as its use is becoming increasingly required in professional and personal settings but it remains largely understudied in…
Descriptors: English, Native Language, Writing (Composition), Word Processing
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – International Journal of Artificial Intelligence in Education, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Sang-Gu Kang – Journal of Pan-Pacific Association of Applied Linguistics, 2024
This paper reports on a young Korean boy's target-like and non-target-like uses of the Korean negation marker "ani" to express various types of negation in Korean, observed approximately between the ages of 2;2 and 2;5. Besides the target-like usage of "ani" as a sentential adverb for a 'no' response, he used "ani" in…
Descriptors: Korean, Morphemes, Toddlers, Language Acquisition
John N. Williams; Yuyan Xue – Studies in Second Language Acquisition, 2024
Is it possible to acquire a sensitivity to a regularity in language without intending to and without awareness of what it is? In this conceptual replication and extension of an earlier study (Williams, 2005) participants were trained on a semiartificial language in which determiner choice was dependent on noun animacy. Participants who did not…
Descriptors: Language Acquisition, Artificial Languages, Intuition, Nouns
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Irena Lovcevic; Denis Burnham; Marina Kalashnikova – Language Learning and Development, 2024
There is a long-standing debate in the literature about the benefits that acoustic components of Infant Directed Speech (IDS) might have for infants' language acquisition. One of the highly contested features is vowel space expansion, which refers to the enlargement of the acoustic space between the corner vowels /i, u, a/ in IDS compared to Adult…
Descriptors: Foreign Countries, Infants, Monolingualism, Speech Communication
Diego G. Campos; Tim Fütterer; Thomas Gfrörer; Rosa Lavelle-Hill; Kou Murayama; Lars König; Martin Hecht; Steffen Zitzmann; Ronny Scherer – Educational Psychology Review, 2024
Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear…
Descriptors: Artificial Intelligence, Algorithms, Computer System Design, Natural Language Processing
John Y. H. Bai; Olaf Zawacki-Richter; Wolfgang Muskens – Turkish Online Journal of Distance Education, 2024
Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Governance
Galit Agmon; Sameer Pradhan; Sharon Ash; Naomi Nevler; Mark Liberman; Murray Grossman; Sunghye Cho – Journal of Speech, Language, and Hearing Research, 2024
Purpose: Multiple methods have been suggested for quantifying syntactic complexity in speech. We compared eight automated syntactic complexity metrics to determine which best captured verified syntactic differences between old and young adults. Method: We used natural speech samples produced in a picture description task by younger (n = 76, ages…
Descriptors: Young Adults, Older Adults, Undergraduate Students, Caregivers
Lena Schmidt; Saleh Mohamed; Nick Meader; Jaume Bacardit; Dawn Craig – Research Synthesis Methods, 2024
The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…
Descriptors: Automation, Natural Language Processing, Literature Reviews, Data Collection
Shawn Hemelstrand; Brian W. L. Wong; Catherine McBride; Urs Maurer; Tomohiro Inoue – Scientific Studies of Reading, 2024
Purpose: We examined the effect of character complexity on early Chinese literacy (word reading and writing). We also investigated whether cognitive skills (phonological awareness, morphological awareness, and rapid automatized naming [RAN]) could moderate the influence of character complexity on literacy outcomes. Method: Our pre-registered study…
Descriptors: Foreign Countries, Elementary School Students, Chinese, Emergent Literacy
Ambre Denis-Noël; Pascale Colé; Deirdre Bolger; Chotiga Pattamadilok – Scientific Studies of Reading, 2024
Purpose: In adults with dyslexia (DYS), the persistent influence of phonological deficits on spoken language processing has mainly been examined in either perceptual tasks or those tapping complex cognitive operations. Much less attention is devoted to spoken word recognition per se. Our study aimed to fill this gap. Method: Adults with and…
Descriptors: Foreign Countries, College Students, Dyslexia, Language Processing