Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| 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 | 3 |
| At Risk Persons | 1 |
| Barriers | 1 |
| Bibliometrics | 1 |
| Computer Science | 1 |
| Content Analysis | 1 |
| Curriculum | 1 |
| Data Use | 1 |
| Dropout Prevention | 1 |
| More ▼ | |
Source
| Cogent Education | 1 |
| MDRC | 1 |
| ProQuest LLC | 1 |
Author
| Denison, Dakota | 1 |
| Hendra, Richard | 1 |
| Michael Joseph King | 1 |
| Mutlu Cukurova | 1 |
| Preel-Dumas, Camille | 1 |
| Sandra Leaton Gray | 1 |
Publication Type
| Reports - Evaluative | 2 |
| Dissertations/Theses -… | 1 |
| Journal Articles | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Direct linkMichael Joseph King – ProQuest LLC, 2022
This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of…
Descriptors: Data Science, Bibliometrics, Qualitative Research, Statistical Analysis
Peer reviewedSandra 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
Download full textPreel-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
