Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 2 |
Descriptor
| Computation | 2 |
| Elementary School Students | 2 |
| Growth Models | 2 |
| Middle School Students | 2 |
| Scores | 2 |
| Value Added Models | 2 |
| Academic Achievement | 1 |
| Academic Standards | 1 |
| Accuracy | 1 |
| Achievement Gains | 1 |
| COVID-19 | 1 |
| More ▼ | |
Publication Type
| Numerical/Quantitative Data | 1 |
| Reports - Descriptive | 1 |
| Reports - Research | 1 |
Education Level
| Elementary Education | 2 |
| Junior High Schools | 2 |
| Middle Schools | 2 |
| Secondary Education | 2 |
| High Schools | 1 |
Audience
Location
| Missouri | 1 |
| Texas (Austin) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Download full textFazlul, Ishtiaque; Koedel, Cory; Parsons, Eric; Qian, Cheng – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2021
We evaluate the feasibility of estimating test-score growth with a gap year in testing data, informing the scenario when state testing resumes after the 2020 COVID-19-induced test stoppage. Our research design is to simulate a gap year in testing using pre-COVID-19 data--when a true test gap did not occur--which facilitates comparisons of…
Descriptors: Scores, Achievement Gains, Computation, Growth Models
Schmitt, Lisa; Hutchins, Shaun – Online Submission, 2016
This report provides an overview of the process used to derive a school's growth level and summarizes 2015 math and reading/ELA growth levels for all AISD elementary, middle and high schools. Additionally, longitudinal data are provided for each school level.
Descriptors: School Districts, Academic Achievement, Elementary School Students, Middle School Students
