Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
Source
| MDRC | 2 |
Author
| Aceves, Aurelia De La Rosa | 1 |
| Denison, Dakota | 1 |
| Hendra, Richard | 1 |
| Preel-Dumas, Camille | 1 |
| Tomlinson, Gloria | 1 |
| Yang, Edith | 1 |
Publication Type
| Guides - General | 1 |
| Reports - Descriptive | 1 |
| Reports - Evaluative | 1 |
Education Level
Audience
Location
| New York | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Preel-Dumas, Camille; Hendra, Richard; Denison, Dakota – MDRC, 2023
This brief explores data science methods that workforce programs can use to predict participant success. With access to vast amounts of data on their programs, workforce training providers can leverage their management information systems (MIS) to understand and improve their programs' outcomes. By predicting which participants are at greater risk…
Descriptors: Labor Force Development, Programs, Prediction, Success
Yang, Edith; Aceves, Aurelia De La Rosa; Tomlinson, Gloria – MDRC, 2019
Workforce development organizations often find it challenging to assess how former program participants are faring in the labor market, since they need to rely on participants' willingness to report and verify their job placements after they either leave or complete their programs. The 2013 Unemployment Insurance Data Sharing Bill (S5773A) amended…
Descriptors: Community Organizations, Labor Force Development, Data Use, Unemployment


