Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 3 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Mathematics Instruction | 4 |
| Readability Formulas | 4 |
| Reading Comprehension | 4 |
| Comparative Analysis | 3 |
| Artificial Intelligence | 2 |
| Computational Linguistics | 2 |
| Computer Software | 2 |
| Correlation | 2 |
| Course Content | 2 |
| Educational Change | 2 |
| Elementary Secondary Education | 2 |
| More ▼ | |
Author
| April Murphy | 2 |
| Husni Almoubayyed | 2 |
| Kole A. Norberg | 2 |
| Kyle Weldon | 2 |
| Logan De Ley | 2 |
| Steve Ritter | 2 |
| Campbell, Anne | 1 |
| Michael J. D. Tulino | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
| Dissertations/Theses -… | 1 |
| Information Analyses | 1 |
| Reports - Evaluative | 1 |
Education Level
| Elementary Secondary Education | 2 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Michael J. D. Tulino – ProQuest LLC, 2024
Researchers have demonstrated a much-needed shift in pedagogical practices to incorporate literacy strategies. This dissertation provides additional empirical evidence to expand the body of research by utilizing a comparative readability analysis that examines the academic language of a popular calculus textbook. A sample from the published…
Descriptors: Mathematics Instruction, Reading Comprehension, Calculus, Academic Language
Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – International Journal of Artificial Intelligence in Education, 2025
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Peer reviewedCampbell, Anne – Journal of Reading, 1979
Explains why applying a readability formula to a content text is not enough to determine whether a student can read and understand the book. (MKM)
Descriptors: Content Area Reading, Mathematics Instruction, Readability, Readability Formulas

Direct link
