Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 2 |
Descriptor
| Progress Monitoring | 2 |
| Student Needs | 2 |
| Access to Information | 1 |
| Alignment (Education) | 1 |
| At Risk Students | 1 |
| Capacity Building | 1 |
| Classification | 1 |
| College Students | 1 |
| Data | 1 |
| Data Collection | 1 |
| Disabilities | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 1 |
| Reports - Descriptive | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Virginia | 2 |
| North Carolina | 1 |
| Pennsylvania | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkCano, Alberto; Leonard, John D. – IEEE Transactions on Learning Technologies, 2019
Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…
Descriptors: Progress Monitoring, At Risk Students, Disproportionate Representation, Underachievement
Download full textRuedel, Kristin; Nelson, Gena; Weingarten, Zach; Bailey, Tessie – National Center for Systemic Improvement at WestEd, 2018
Data-based problem solving within a multi-tiered system of support (MTSS) can help state education agencies and local education agencies (LEAs) improve graduation rates for students with disabilities. To facilitate problem solving within an MTSS, states and LEAs need efficient systems and processes for analyzing and integrating data across…
Descriptors: Disabilities, Graduation Rate, Problem Solving, Early Intervention
