Data Generation using a Probabilistic Auto-Regressive Model with Application to Student Exam Performance Analysis

被引:0
作者
Chan, Jackson Tsz Wah [1 ]
Chui, Kwok Tai [1 ]
Lee, Lap-Kei [1 ]
Paoprasert, Naraphom [2 ]
Ng, Kwan-Keung [3 ]
机构
[1] Hong Kong Metropolitan Univ, Sch Sci & Technol, Hong Kong, Peoples R China
[2] Kasetsart Univ, Fac Engn, Bangkok, Thailand
[3] Ming Ai London Inst, London, England
来源
2024 INTERNATIONAL SYMPOSIUM ON EDUCATIONAL TECHNOLOGY, ISET | 2024年
关键词
At-risk students; autoregressive model; data generation; data synthesis; machine learning; student exam; ADVERSARIAL NETWORK;
D O I
10.1109/ISET61814.2024.00026
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Exam scores are usually the most important assessment criterion for evaluating students' understanding of course materials and learning outcomes. Mid-term scores can serve as an indicator to predict exam scores because these assessments are generally common in the format of questions, closed-book assessments, and covering a variety of course topics. Machine learning algorithms have become a promising educational technology for forecasting student exam scores. If the prediction model suggests that a student is academically at risk, additional tutorial sessions and academic advice are potential follow-up actions. However, there are several research challenges: (i) mid-term scores are small-scale with limited samples, (ii) many physiological signals are required to supplement midterm and final exam scores as a multimodal problem, and (iii) insufficient investigation of the length of physiological signals. This paper employed a probabilistic autoregressive (PAR) model to generate Photoplethysmography (PPG) signals. A Wearable Exam Stress Dataset was used to benchmark the model. Results revealed that the PAR model can synthesize high-quality PPG signals in midterm and final exam scores to support educational research, particularly in the student exam performance analysis. Future research directions are suggested to further explore data generation research in education.
引用
收藏
页码:87 / 90
页数:4
相关论文
共 25 条
[1]  
[Anonymous], 2023, SUSTAINABLE DEV GOAL
[2]   Student dropout at university: a phase-orientated view on quitting studies and changing majors [J].
Baeulke, Lisa ;
Grunschel, Carola ;
Dresel, Markus .
EUROPEAN JOURNAL OF PSYCHOLOGY OF EDUCATION, 2022, 37 (03) :853-876
[3]   Educational data mining to predict students' academic performance: A survey study [J].
Batool, Saba ;
Rashid, Junaid ;
Nisar, Muhammad Wasif ;
Kim, Jungeun ;
Kwon, Hyuk-Yoon ;
Hussain, Amir .
EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (01) :905-971
[4]   A DeepAR based hybrid probabilistic prediction model for production bottleneck of flexible shop-floor in Industry 4.0 [J].
Chang, Xiao ;
Jia, Xiaoliang .
COMPUTERS & INDUSTRIAL ENGINEERING, 2023, 185
[5]   Semisupervised anomaly detection of multivariate time series based on a variational autoencoder [J].
Chen, Ningjiang ;
Tu, Huan ;
Duan, Xiaoyan ;
Hu, Liangqing ;
Guo, Chengxiang .
APPLIED INTELLIGENCE, 2023, 53 (05) :6074-6098
[6]  
Chui K. T., 2023, INT C TECHN ED BANGK, P242
[7]   Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset [J].
Chui, Kwok Tai ;
Lytras, Miltiadis D. ;
Vasant, Pandian .
APPLIED SCIENCES-BASEL, 2020, 10 (13)
[8]   Predicting Students Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine [J].
Chui, Kwok Tai ;
Liu, Ryan Wen ;
Zhao, Mingbo ;
De Pablos, Patricia Ordonez .
IEEE ACCESS, 2020, 8 :86745-86752
[9]   Predicting at-risk university students in a virtual learning environment via a machine learning algorithm [J].
Chui, Kwok Tai ;
Fung, Dennis Chun Lok ;
Lytras, Miltiadis D. ;
Lam, Tin Miu .
COMPUTERS IN HUMAN BEHAVIOR, 2020, 107
[10]   Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining-A survey [J].
Dol, Sunita M. ;
Jawandhiya, Pradip M. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 122