Publication Date
| In 2025 | 0 |
| Since 2024 | 2 |
| Since 2021 (last 5 years) | 2 |
| Since 2016 (last 10 years) | 6 |
| Since 2006 (last 20 years) | 6 |
Descriptor
Source
| Grantee Submission | 2 |
| International Journal of… | 1 |
| Journal of Language and… | 1 |
| Language Learning Journal | 1 |
| UK Department for Education | 1 |
Author
| Balyan, Renu | 3 |
| McCarthy, Kathryn S. | 3 |
| McNamara, Danielle S. | 3 |
| Salwa Mohamed | 1 |
| Sügümlü, Üzeyir | 1 |
| Teksan, Keziban | 1 |
| Çinpolat, Enes | 1 |
Publication Type
| Reports - Research | 5 |
| Journal Articles | 4 |
| Information Analyses | 1 |
| Reports - Evaluative | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Elementary Education | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
| Teachers | 1 |
Location
| Turkey | 1 |
| United Kingdom | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Flesch Kincaid Grade Level… | 3 |
| Flesch Reading Ease Formula | 1 |
What Works Clearinghouse Rating
Peer reviewed
Direct linkSalwa Mohamed – Language Learning Journal, 2024
Text classification and text gradation are important for language teachers. Profiling and readability studies examine textual and linguistic features that determine text difficulty. Arabic, as an under-resourced language, suffers from a lack of such studies which results in material developers and textbook writers relying on their intuitions and…
Descriptors: Classification, Language Teachers, Arabic, Second Language Learning
Download full textTeksan, Keziban; Sügümlü, Üzeyir; Çinpolat, Enes – Journal of Language and Linguistic Studies, 2020
The present study aims to determine the readability and clarity levels of certain Turkish tales for middle-school students. The study was conducted with a survey model. The study material consists of three tales each chosen from the books titled "Az Gittik Uz Gittik" and "Masal Masal Içinde," and the study group consists of 90…
Descriptors: Readability Formulas, Readability, Turkish, Middle School Students
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
UK Department for Education, 2024
This report sets out the findings of the technical development work completed as part of the Use Cases for Generative AI in Education project, commissioned by the Department for Education (DfE) in September 2023. It has been published alongside the User Research Report, which sets out the findings from the ongoing user engagement activity…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Software, Computational Linguistics
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2018
While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using…
Descriptors: Artificial Intelligence, Classification, Comparative Analysis, Prediction
