Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Artificial Intelligence | 4 |
Readability | 4 |
Readability Formulas | 4 |
Classification | 3 |
Natural Language Processing | 3 |
Comparative Analysis | 2 |
Difficulty Level | 2 |
Intelligent Tutoring Systems | 2 |
Man Machine Systems | 2 |
Reading Comprehension | 2 |
Accuracy | 1 |
More ▼ |
Author
Balyan, Renu | 3 |
McCarthy, Kathryn S. | 3 |
McNamara, Danielle S. | 3 |
Arun-Balajiee… | 1 |
Jeevan Chapagain | 1 |
Mohammad Hassany | 1 |
Priti Oli | 1 |
Rabin Banjade | 1 |
Vasile Rus | 1 |
Publication Type
Reports - Research | 4 |
Journal Articles | 2 |
Speeches/Meeting Papers | 2 |
Information Analyses | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Flesch Kincaid Grade Level… | 3 |
Flesch Reading Ease Formula | 1 |
What Works Clearinghouse Rating

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
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
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