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...

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Bibliographic Details
Published in2024 International Symposium on Educational Technology (ISET) pp. 75 - 79
Main Authors Chan, Jackson Tsz Wah, Chui, Kwok Tai, Wong, Leung Pun, Liu, Ryan Wen, Ng, Kwan-Keung, Hui, Yan Keung
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.07.2024
Subjects
Copula
data generation
Data integrity
Educational technology
exam score
socioeconomic factors
Socioeconomics
student performance
Training data
Waste materials
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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