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Heather Allmond Barker; Hollylynne S. Lee; Shaun Kellogg; Robin Anderson – Online Learning, 2024
Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify…
Descriptors: MOOCs, Motivation, Enrollment, Professional Development
Terra Blevins – ProQuest LLC, 2024
While large language models (LLMs) continue to grow in scale and gain new zero-shot capabilities, their performance for languages beyond English increasingly lags behind. This gap is due to the "curse of multilinguality," where multilingual language models perform worse on individual languages than a monolingual model trained on that…
Descriptors: Multilingualism, Computational Linguistics, Second Languages, Reliability
Zixuan Ke – ProQuest LLC, 2024
The essence of human intelligence lies in its ability to learn continuously, accumulating past knowledge to aid in future learning and problem-solving endeavors. In contrast, the current machine learning paradigm often operates in isolation, lacking the capacity for continual learning and adaptation. This deficiency becomes apparent in the face of…
Descriptors: Computational Linguistics, Computer Software, Barriers, Artificial Intelligence
Julian M. Pine; Daniel Freudenthal; Fernand Gobet – Journal of Child Language, 2023
Verb-marking errors are a characteristic feature of the speech of typically-developing (TD) children and are particularly prevalent in the speech of children with Developmental Language Disorder (DLD). However, both the pattern of verb-marking error in TD children and the pattern of verb-marking deficit in DLD vary across languages and interact…
Descriptors: Developmental Disabilities, Language Impairments, Verbs, Error Patterns
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
Frank, Stefan L. – Language Learning, 2021
Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models…
Descriptors: Multilingualism, Language Processing, Computational Linguistics, Psycholinguistics
Towards Automatic Question Generation Using Pre-Trained Model in Academic Field for Bahasa Indonesia
Derwin Suhartono; Muhammad Rizki Nur Majiid; Renaldy Fredyan – Education and Information Technologies, 2024
Exam evaluations are essential to assessing students' knowledge and progress in a subject or course. To meet learning objectives and assess student performance, questions must be themed. Automatic Question Generation (AQG) is our novel approach to this problem. A comprehensive process for autonomously generating Bahasa Indonesia text questions is…
Descriptors: Foreign Countries, Computational Linguistics, Computer Software, Questioning Techniques
Dadi Ramesh; Suresh Kumar Sanampudi – European Journal of Education, 2024
Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…
Descriptors: Scoring, Essays, Writing Evaluation, Memory
Wang, Jue; Engelhard, George; Combs, Trenton – Journal of Experimental Education, 2023
Unfolding models are frequently used to develop scales for measuring attitudes. Recently, unfolding models have been applied to examine rater severity and accuracy within the context of rater-mediated assessments. One of the problems in applying unfolding models to rater-mediated assessments is that the substantive interpretations of the latent…
Descriptors: Writing Evaluation, Scoring, Accuracy, Computational Linguistics
Huteng Dai – ProQuest LLC, 2024
In this dissertation, I establish a research program that uses computational modeling as a testbed for theories of phonological learning. This dissertation focuses on a fundamental question: how do children acquire sound patterns from noisy, real-world data, especially in the presence of lexical exceptions that defy regular patterns? For instance,…
Descriptors: Phonology, Language Acquisition, Computational Linguistics, Linguistic Theory
Caroline F. Rowland; Amy Bidgood; Gary Jones; Andrew Jessop; Paula Stinson; Julian M. Pine; Samantha Durrant; Michelle S. Peter – Language Learning, 2025
A strong predictor of children's language is performance on non-word repetition (NWR) tasks. However, the basis of this relationship remains unknown. Some suggest that NWR tasks measure phonological working memory, which then affects language growth. Others argue that children's knowledge of language/language experience affects NWR performance. A…
Descriptors: Vocabulary Development, Comparative Analysis, Computational Linguistics, Language Skills
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
Aakriti Kumar; Aaron S. Benjamin; Andrew Heathcote; Mark Steyvers – npj Science of Learning, 2022
Practice in real-world settings exhibits many idiosyncrasies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied…
Descriptors: Comparative Analysis, Computational Linguistics, Learning Processes, Computer Games
Ruseti, Stefan; Dascalu, Maria-Dorinela; Corlatescu, Dragos-Georgian; Dascalu, Mihai; Trausan-Matu, Stefan; McNamara, Danielle S. – Grantee Submission, 2021
Dialogism is a philosophical theory centered on the idea that life involves a dialogue among multiple voices in a continuous exchange and interaction. Considering human language, different ideas or points of view take the form of voices, which spread throughout any discourse and influence it. From a computational point of view, voices can be…
Descriptors: Dialogs (Language), Computational Linguistics, Semantics, Models
Wang, Heqiao; Troia, Gary A. – Written Communication, 2023
The primary purpose of this study is to investigate the degree to which register knowledge, register-specific motivation, and diverse linguistic features are predictive of human judgment of writing quality in three registers--narrative, informative, and opinion. The secondary purpose is to compare the evaluation metrics of register-partitioned…
Descriptors: Writing Evaluation, Essays, Elementary School Students, Grade 4