Student Performance Predictions for Advanced Engineering Mathematics Course with New Multivariate Copula Models

Thong Nguyen-Huy; Ravinesh C Deo; Shahjahan Khan; Aruna Devi; Adewuyi Ayodele Adeyinka; Armando A Apan; Zaher Mundher Yaseen

doi:10.1109/ACCESS.2022.3168322

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Student Performance Predictions for Advanced Engineering Mathematics Course with New Multivariate Copula Models

Journal article

Open access

Peer reviewed

Student Performance Predictions for Advanced Engineering Mathematics Course with New Multivariate Copula Models

Thong Nguyen-Huy, Ravinesh C Deo, Shahjahan Khan, Aruna Devi, Adewuyi Ayodele Adeyinka, Armando A Apan and Zaher Mundher Yaseen

IEEE Access, Vol.10, pp.45112-45136

2022

DOI: https://doi.org/10.1109/ACCESS.2022.3168322

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Abstract

Education assessment and evaluation

Other education not elsewhere classified

Applied statistics

Applications in social sciences and education

Expanding knowledge in education

Expanding knowledge in the mathematical sciences

academic performance

Australia

Biological system modeling

D-vine copula

Data models

Education

education decision-making

engineering mathematics performance prediction

Mathematical models

Mathematics

multivariate probability model

Predictive models

statistical model

Engineering Mathematics requires that problem-solving should be implemented through ongoing assessments; hence the prediction of student performance using continuous assessments remains an important task for engineering educators, mainly to monitor and improve their teaching practice. This paper develops probabilistic models to predict weighted scores (WS, or the overall mark leading to a final grade) for face-to-face (on-campus) and web-based (online) Advanced Engineering Mathematics students at an Australian regional university over a 6-year period (2013-2018). We fitted parametric and non-parametric D-vine copula models utilizing multiple quizzes, assignments and examination score results to construct and validate the predicted WS in independently test datasets. The results are interpreted in terms of the probability of whether a student's continuous performance (i.e., individually or jointly with other counterpart assessments) is likely to lead to a passing grade conditional upon joint performance in students' quizzes and assignment scores. The results indicate that the newly developed D-vine model, benchmarked against a linear regression model, can generate accurate grade predictions, and particularly handle the problem of low or high scores (tail dependence) compared with a conventional model for both face-to-face, and web-based students. Accordingly, the findings advocate the practical utility of joint copula models that capture the dependence structure in engineering mathematics students' marks achieved. This therefore, provide insights through learning analytic methods to support an engineering educator's teaching decisions. The implications are on better supporting engineering mathematics students' success and retention, developing evidence-based strategies consistent with engineering graduate requirements through improved teaching and learning, and identifying/addressing the risk of failure through early intervention. The proposed methods can guide an engineering educator's practice by investigating joint influences of engineering problem-solving assessments on their student's grades.

Details

Title: Student Performance Predictions for Advanced Engineering Mathematics Course with New Multivariate Copula Models
Authors: Thong Nguyen-Huy (Author) - University of Southern Queensland
Ravinesh C Deo (Author) - University of Southern Queensland
Shahjahan Khan (Author) - University of Southern Queensland
Aruna Devi (Author) - University of the Sunshine Coast, Queensland, School of Education and Tertiary Access
Adewuyi Ayodele Adeyinka (Author) - University of Southern Queensland
Armando A Apan (Author) - University of Southern Queensland
Zaher Mundher Yaseen (Author) - University of Southern Queensland
Publication details: IEEE Access, Vol.10, pp.45112-45136
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2022
DOI: 10.1109/ACCESS.2022.3168322
ISSN: 2169-3536
Copyright note: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Grant note: This work was supported in part by the University of Southern Queensland (School of Sciences Q1 Challenge Grant 2018), and in part by the Office for Advancement of Learning and Teaching Technology Demonstrator Project Artificial Intelligence Predictive Analytics Framework under the Ethics Approval Number H18REA236.
Organisation Unit: Indigenous and Transcultural Research Centre; School of Education and Tertiary Access; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99643279202621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications