Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
Descriptor
| Academic Achievement | 2 |
| Computer Science Education | 2 |
| Program Design | 2 |
| Age | 1 |
| Algorithms | 1 |
| Educational Attainment | 1 |
| Empowerment | 1 |
| Gender Differences | 1 |
| High Achievement | 1 |
| Intervention | 1 |
| Least Squares Statistics | 1 |
| More ▼ | |
Author
| Andrés Castro Samayoa | 1 |
| Angela Boatman | 1 |
| Aykut Durak | 1 |
| Bingran Zeng | 1 |
| Ryan Creps | 1 |
| Shadman Islem | 1 |
| Vahide Bulut | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Tech Equity: A Survival Analysis of an Undergraduate Computer Science Supplemental Education Program
Peer reviewed
Direct linkRyan Creps; Shadman Islem; Bingran Zeng; Angela Boatman; Andrés Castro Samayoa – Innovative Higher Education, 2025
This study examines the success of undergraduate students in computer science supplementary courses offered by a non-profit organization in partnership with colleges and universities across the U.S. Using a novel dataset from the nonprofit organization, we present one of the first descriptive overviews of students enrolled in supplemental computer…
Descriptors: Undergraduate Study, Program Evaluation, Computer Science Education, Supplementary Education
Aykut Durak; Vahide Bulut – Technology, Knowledge and Learning, 2025
The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the…
Descriptors: Prediction, Low Achievement, High Achievement, Academic Achievement
