NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 20253
Since 20248
Since 2021 (last 5 years)18
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 18 results Save | Export
Mishra, Swaroop – ProQuest LLC, 2023
Humans have the remarkable ability to solve different tasks by simply reading textual instructions that define the tasks and looking at a few examples. Natural Language Processing (NLP) models built with the conventional machine learning paradigm, however, often struggle to generalize across tasks (e.g., a question-answering system cannot solve…
Descriptors: Natural Language Processing, Models, Readability, Mathematical Logic
Peer reviewed Peer reviewed
Direct linkDirect link
Suna-Seyma Uçar; Itziar Aldabe; Nora Aranberri; Ana Arruarte – International Journal of Artificial Intelligence in Education, 2024
Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help…
Descriptors: Artificial Intelligence, Technology Uses in Education, Automation, Readability
Peer reviewed Peer reviewed
Direct linkDirect link
Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics
Peer reviewed Peer reviewed
Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
Renu Balyan; Danielle S. McNamara; Scott A. Crossley; William Brown; Andrew J. Karter; Dean Schillinger – Grantee Submission, 2022
Online patient portals that facilitate communication between patient and provider can improve patients' medication adherence and health outcomes. The effectiveness of such web-based communication measures can be influenced by the health literacy (HL) of a patient. In the context of diabetes, low HL is associated with severe hypoglycemia and high…
Descriptors: Computational Linguistics, Patients, Physicians, Information Security
Peer reviewed Peer reviewed
Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
Botarleanu, Robert-Mihai; Dascalu, Mihai; Watanabe, Micah; McNamara, Danielle S.; Crossley, Scott Andrew – Grantee Submission, 2021
The ability to objectively quantify the complexity of a text can be a useful indicator of how likely learners of a given level will comprehend it. Before creating more complex models of assessing text difficulty, the basic building block of a text consists of words and, inherently, its overall difficulty is greatly influenced by the complexity of…
Descriptors: Multilingualism, Language Acquisition, Age, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Scott Crossley; Joon Suh Choi – Reading Psychology, 2024
This paper examines links between perfect rhymes and text readability and decoding using a measure of English rhymes called the Perfect Rhymes Dictionary (PeRDict). PeRDict is based on the Carnegie Mellon University Pronouncing Dictionary (the CMUdict) and provides rhyme counts for ~48,000 words in English and for the most frequent 1,000, 2,500,…
Descriptors: Measurement Techniques, Phonology, Pronunciation, Dictionaries
Peer reviewed Peer reviewed
Direct linkDirect link
Xuefan Li; Tingsong Li; Minjuan Wang; Sining Tao; Xiaoxu Zhou; Xiaoqing Wei; Naiqing Guan – IEEE Transactions on Learning Technologies, 2025
With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students' analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically…
Descriptors: Artificial Intelligence, Technology Uses in Education, High School Students, Foreign Countries
Peer reviewed Peer reviewed
Direct linkDirect link
Xiaopeng Zhang; Xiaofei Lu – Language Learning, 2024
This study examined the relationship of linguistic complexity, captured using a set of lexical richness, syntactic complexity, and discoursal complexity indices, to second language (L2) learners' perception of text difficulty, captured using L2 raters' comparative judgment on text comprehensibility and reading speed. Testing materials were 180…
Descriptors: Syntax, Second Language Learning, Second Language Instruction, Decision Making
Peer reviewed Peer reviewed
Direct linkDirect link
Taegang Lee; Yoonhyoung Lee; Sungmook Choi – Language Learning & Technology, 2025
Empirical evidence remains sparse about how videos enhanced with first-language (L1) and second-language (L2) subtitles influence cognitive load in L2 learners. To address this point, 25 Korean undergraduate students were exposed to six short videos: baseline, L1-subtitled, and L2-subtitled videos at both high and low difficulty levels (determined…
Descriptors: Captions, Native Language, Second Language Learning, Language Processing
Peer reviewed Peer reviewed
Direct linkDirect link
Nahatame, Shingo – Language Learning, 2021
Although text readability has traditionally been measured based on simple linguistic features, recent studies have employed natural language processing techniques to develop new readability formulas that better represent theoretical accounts of reading processes. This study evaluated the construct validity of different readability formulas,…
Descriptors: Readability, Natural Language Processing, Readability Formulas, Reading Processes
Peer reviewed Peer reviewed
PDF on ERIC Download full text
McCarthy, Philip M.; Kaddoura, Noor W.; Al-Harthy, Ayah; Thomas, Anuja M.; Duran, Nicholas D.; Ahmed, Khawlah – Pegem Journal of Education and Instruction, 2022
This study analyzes the linguistic features of counter-arguments and support arguments using two computational linguistic tools: Coh-Metrix and Gramulator. The research question investigates whether counter-argument paragraphs and support paragraphs are different in terms of their linguistic features. To conduct this study, a corpus of 78…
Descriptors: Computational Linguistics, Connected Discourse, Discourse Analysis, Readability
Peer reviewed Peer reviewed
Direct linkDirect link
Ya-Mei Chen – Interpreter and Translator Trainer, 2024
This study explores the way translation crowdsourcing may contribute to metacognitive translator training through a teaching experiment where Global Voices Lingua was integrated into an undergraduate English-Chinese translation course. In doing so, the study investigates how translation students' awareness of conditional knowledge and its…
Descriptors: Translation, Metacognition, Second Languages, Language Processing
DeKita G. Moon Rembert – ProQuest LLC, 2021
Many students may find math word problems uninteresting; therefore, lacking the motivation to solve them. The content in most math word problems in use today is outdated, deliberately generic, and does not fully engage students. The development of technologies that personalize math word problems seeks to improve the engagement of students.…
Descriptors: Word Problems (Mathematics), Student Interests, Learner Engagement, Educational Technology
Previous Page | Next Page »
Pages: 1  |  2