Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Artificial Intelligence | 3 |
Data Science | 3 |
Research Methodology | 2 |
At Risk Persons | 1 |
Barriers | 1 |
Content Analysis | 1 |
Cooperative Learning | 1 |
Data Use | 1 |
Dropout Prevention | 1 |
Economic Development | 1 |
Economics | 1 |
More ▼ |
Author
Chris Hobson | 1 |
David Rae | 1 |
Denison, Dakota | 1 |
Edward Cartwright | 1 |
Harsh Shah | 1 |
Hendra, Richard | 1 |
Mario Gongora | 1 |
Mutlu Cukurova | 1 |
Preel-Dumas, Camille | 1 |
Sandra Leaton Gray | 1 |
Publication Type
Reports - Evaluative | 3 |
Journal Articles | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
David Rae; Edward Cartwright; Mario Gongora; Chris Hobson; Harsh Shah – Industry and Higher Education, 2024
This paper demonstrates how the innovative application of a Collective Intelligence approach enhanced Local Skills Improvement Planning information for employers, education and skills training organisations and regional economic policy organisations. This took place within a Knowledge Transfer Partnership between a Chamber of Commerce and a…
Descriptors: Cooperative Learning, Intelligence, Knowledge Management, Skill Development
Sandra Leaton Gray; Mutlu Cukurova – Cogent Education, 2024
Debates surrounding the use of data science in educational AI are frequently rather entrenched, revolving around commercial models and talk of teacher replacement. This article explores the potential for digital textual analysis within humanities and social science education, advocating for a sociologically-driven approach that complements, rather…
Descriptors: Humanities, Social Sciences, Social Science Research, Research Methodology
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