Publication Date
In 2025 | 1 |
Since 2024 | 12 |
Since 2021 (last 5 years) | 24 |
Since 2016 (last 10 years) | 35 |
Since 2006 (last 20 years) | 43 |
Descriptor
Computer Software | 47 |
Models | 47 |
Natural Language Processing | 31 |
Artificial Intelligence | 21 |
Computational Linguistics | 19 |
Language Processing | 19 |
Prediction | 17 |
Comparative Analysis | 13 |
Foreign Countries | 12 |
Second Language Learning | 11 |
Semantics | 11 |
More ▼ |
Source
Author
Dascalu, Mihai | 4 |
McNamara, Danielle S. | 4 |
Danielle S. McNamara | 2 |
Dasgupta, Ranjan | 2 |
Desmarais, Michel, Ed. | 2 |
Mihai Dascalu | 2 |
Romero, Cristobal, Ed. | 2 |
Sengupta, Souvik | 2 |
Adi, Tom | 1 |
Allen, Laura K. | 1 |
Andres Neyem | 1 |
More ▼ |
Publication Type
Education Level
Higher Education | 14 |
Postsecondary Education | 12 |
Elementary Education | 6 |
Junior High Schools | 5 |
Middle Schools | 5 |
Secondary Education | 5 |
Elementary Secondary Education | 4 |
High Schools | 4 |
Grade 9 | 3 |
Grade 10 | 2 |
Grade 8 | 2 |
More ▼ |
Audience
Researchers | 2 |
Administrators | 1 |
Students | 1 |
Teachers | 1 |
Location
Brazil | 3 |
Germany | 3 |
Netherlands | 3 |
South Korea | 3 |
Australia | 2 |
Israel | 2 |
Pennsylvania | 2 |
Portugal | 2 |
Spain | 2 |
Asia | 1 |
Canada | 1 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
Dale Chall Readability Formula | 1 |
Flesch Kincaid Grade Level… | 1 |
Flesch Reading Ease Formula | 1 |
Massachusetts Comprehensive… | 1 |
Program for International… | 1 |
Test of English for… | 1 |
What Works Clearinghouse Rating
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
Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
Paul Meara; Imma Miralpeix – Vocabulary Learning and Instruction, 2025
This paper is part 5 of a series of workshops that examines the properties of some simple models of vocabulary networks. While previous workshops dealt with activating words in the network, this workshop focuses on vocabulary loss. We will simulate two possible ways of modelling attrition: (a) explicitly turning active words OFF, and (b) raising…
Descriptors: Vocabulary Development, Workshops, Models, Networks
Masato Nakamura; Shota Momma; Hiromu Sakai; Colin Phillips – Cognitive Science, 2024
Comprehenders generate expectations about upcoming lexical items in language processing using various types of contextual information. However, a number of studies have shown that argument roles do not impact neural and behavioral prediction measures. Despite these robust findings, some prior studies have suggested that lexical prediction might be…
Descriptors: Diagnostic Tests, Nouns, Language Processing, Verbs
Bogdan Nicula; Mihai Dascalu; Tracy Arner; Renu Balyan; Danielle S. McNamara – Grantee Submission, 2023
Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…
Descriptors: Reading Comprehension, Language Processing, Models, STEM Education
Margaret A.L. Blackie – Teaching in Higher Education, 2024
Large language models such as ChatGPT can be seen as a major threat to reliable assessment in higher education. In this point of departure, I argue that these tools are a major game changer for society at large. Many of the jobs we now consider highly skilled are based on pattern recognition that can much more reliably be carried by fine-tuned…
Descriptors: Artificial Intelligence, Synchronous Communication, Science and Society, Evaluation
Sinclair, Jeanne; Jang, Eunice Eunhee; Rudzicz, Frank – Journal of Educational Psychology, 2021
Advances in machine learning (ML) are poised to contribute to our understanding of the linguistic processes associated with successful reading comprehension, which is a critical aspect of children's educational success. We used ML techniques to investigate and compare associations between children's reading comprehension and 260 linguistic…
Descriptors: Prediction, Reading Comprehension, Natural Language Processing, Speech Communication
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
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software
Yi Gui – ProQuest LLC, 2024
This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…
Descriptors: Writing Tests, Natural Language Processing, Writing Evaluation, Scoring
Gloria Ashiya Katuka – ProQuest LLC, 2024
Dialogue act (DA) classification plays an important role in understanding, interpreting and modeling dialogue. Dialogue acts (DAs) represent the intended meaning of an utterance, which is associated with the illocutionary force (or the speaker's intention), such as greetings, questions, requests, statements, and agreements. In natural language…
Descriptors: Dialogs (Language), Classification, Intention, Natural Language Processing
Rao, Dhawaleswar; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2020
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's.…
Descriptors: Multiple Choice Tests, Test Construction, Automation, Computer Software
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability