Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 2 |
Descriptor
Algorithms | 2 |
Predictor Variables | 2 |
College Planning | 1 |
College Students | 1 |
Course Selection (Students) | 1 |
Data Use | 1 |
Decision Making | 1 |
Enrollment Trends | 1 |
Equal Education | 1 |
Information Retrieval | 1 |
Majors (Students) | 1 |
More ▼ |
Source
AERA Open | 2 |
Author
Dalal, Nathan | 1 |
Denisa Gándara | 1 |
Hadis Anahideh | 1 |
Lang, David | 1 |
Lorenzo Picchiarini | 1 |
Matthew P. Ison | 1 |
Paepcke, Andreas | 1 |
Stevens, Mitchell L. | 1 |
Wang, Alex | 1 |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Denisa Gándara; Hadis Anahideh; Matthew P. Ison; Lorenzo Picchiarini – AERA Open, 2024
Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine…
Descriptors: Algorithms, Social Bias, Minority Groups, Equal Education
Lang, David; Wang, Alex; Dalal, Nathan; Paepcke, Andreas; Stevens, Mitchell L. – AERA Open, 2022
Committing to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major issuance remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe…
Descriptors: Undergraduate Students, Majors (Students), College Planning, Natural Language Processing