Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Computer Science Education | 1 |
| Correlation | 1 |
| Gender Differences | 1 |
| Grade Point Average | 1 |
| Grades (Scholastic) | 1 |
| Grading | 1 |
| Majors (Students) | 1 |
| Predictor Variables | 1 |
| Required Courses | 1 |
| STEM Education | 1 |
| Scores | 1 |
| More ▼ | |
Source
| ACM Transactions on Computing… | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Illinois | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkTomkin, Jonathan H.; West, Matthew; Herman, Geoffrey L. – ACM Transactions on Computing Education, 2018
We present a methodological improvement for calculating Grade Point Averages (GPAs). Heterogeneity in grading between courses systematically biases observed GPAs for individual students: the GPA observed depends on course selection. We show how a logistic model can account for course selection by simulating how every student in a sample would…
Descriptors: Grade Point Average, Grading, Predictor Variables, Grades (Scholastic)
