Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Audience Response Systems | 1 |
| Blended Learning | 1 |
| Elementary School Mathematics | 1 |
| Elementary School Students | 1 |
| Mathematics Achievement | 1 |
| Natural Language Processing | 1 |
| Pretests Posttests | 1 |
| Self Concept | 1 |
Author
| Baker, Ryan S. | 1 |
| Crossley, Scott A. | 1 |
| Karumbaiah, Shamya | 1 |
| Labrum, Matthew J. | 1 |
| Ocumpaugh, Jaclyn | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
| Tests/Questionnaires | 1 |
Education Level
| Elementary Education | 1 |
Audience
Location
| Texas | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textCrossley, Scott A.; Karumbaiah, Shamya; Ocumpaugh, Jaclyn; Labrum, Matthew J.; Baker, Ryan S. – Journal of Learning Analytics, 2020
This study builds on prior research by leveraging natural language processing (NLP), click-stream analyses, and survey data to predict students' mathematics success and math identity (namely, self-concept, interest, and value of mathematics). Specifically, we combine NLP tools designed to measure lexical sophistication, text cohesion, and…
Descriptors: Elementary School Mathematics, Blended Learning, Self Concept, Audience Response Systems
