@inproceedings{d7d3003ae4fd4fa3b32f89ea195c06ff,
title = "Generating Multivariate Exam Scores using Copulas and Socioeconomic Factors",
abstract = "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.",
keywords = "Copula, data generation, exam score, socioeconomic factors, student performance",
author = "Chan, {Jackson Tsz Wah} and Chui, {Kwok Tai} and Wong, {Leung Pun} and Liu, {Ryan Wen} and Ng, {Kwan Keung} and Hui, {Yan Keung}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th International Symposium on Educational Technology, ISET 2024 ; Conference date: 29-07-2024 Through 01-08-2024",
year = "2024",
doi = "10.1109/ISET61814.2024.00024",
language = "English",
series = "Proceedings - 2024 International Symposium on Educational Technology, ISET 2024",
pages = "75--79",
editor = "Chui, {Kwok Tai} and Hui, {Yan Keung} and Dingqi Yang and Lap-Kei Lee and Leung-Pun Wong and Reynolds, {Barry Lee}",
booktitle = "Proceedings - 2024 International Symposium on Educational Technology, ISET 2024",
}