Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Classification | 1 |
| College Students | 1 |
| High School Students | 1 |
| Learning Analytics | 1 |
| Markov Processes | 1 |
| Models | 1 |
| Profiles | 1 |
| Reading Comprehension | 1 |
| Reading Strategies | 1 |
| Responses | 1 |
| Science Materials | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Author
| Allen, Laura K. | 1 |
| Butterfuss, Reese | 1 |
| Christhilf, Katerina | 1 |
| Magliano, Joseph P. | 1 |
| McCarthy, Kathryn S. | 1 |
| McNamara, Danielle S. | 1 |
| Newton, Natalie | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| High Schools | 1 |
| Higher Education | 1 |
| Postsecondary Education | 1 |
| Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textChristhilf, Katerina; Newton, Natalie; Butterfuss, Reese; McCarthy, Kathryn S.; Allen, Laura K.; Magliano, Joseph P.; McNamara, Danielle S. – International Educational Data Mining Society, 2022
Prompting students to generate constructed responses as they read provides a window into the processes and strategies that they use to make sense of complex text. In this study, Markov models examined the extent to which: (1) patterns of strategies; and (2) strategy combinations could be used to inform computational models of students' text…
Descriptors: Markov Processes, Reading Strategies, Reading Comprehension, Models
