Generating Multivariate Exam Scores using Copulas and Socioeconomic Factors
Final exams are essential to assessing students' level of understanding of the course materials and evaluating their achievements in the learning outcomes. Many factors may have affected their performance throughout the courses. These are personal-, social-, and economic-related. Student Exam P...
Saved in:
Published in | 2024 International Symposium on Educational Technology (ISET) pp. 75 - 79 |
---|---|
Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
29.07.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Final exams are essential to assessing students' level of understanding of the course materials and evaluating their achievements in the learning outcomes. Many factors may have affected their performance throughout the courses. These are personal-, social-, and economic-related. Student Exam Performance Datasets are usually small-scale. The nature of a typical dataset comprises a small class size and few assessment components, and the students and parents may refuse to disclose and share too much personal information. This paper proposes a copula-based data generation algorithm that provides additional training data for the datasets. The algorithm is evaluated based on eight aspects: diagnostic, data quality, missing value similarity, statistic similarity, category coverage, range coverage, new row synthesis, and column comparison. |
---|---|
ISSN: | 2766-2144 |
DOI: | 10.1109/ISET61814.2024.00024 |