Modern artificial intelligence model development for undergraduate student performance prediction: an investigation on engineering mathematics courses

Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Yaseen, Zaher Mundher and Al-Ansari, Nadhir and Nguyen-Huy, Thong ORCID: https://orcid.org/0000-0002-2201-6666 and Langlands, Trevor and Galligan, Linda ORCID: https://orcid.org/0000-0001-8156-8690 (2020) Modern artificial intelligence model development for undergraduate student performance prediction: an investigation on engineering mathematics courses. IEEE Access.

[img]
Preview
Text (Publisher's Accepted Version)
09145548.pdf
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview

Abstract

A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid- to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector


Statistics for USQ ePrint 39136
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This paper is an outcome of the USQ Technology Demonstrator Project led by A/Prof Ravinesh Deo (2018-2021). It was funded by School of Science Q1 Challenge Grant 2019. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 28 Jul 2020 04:54
Last Modified: 31 Jul 2020 01:52
Uncontrolled Keywords: education decision-making; Extreme Learning Machine; student performance modelling; AI in higher education; engineering mathematics
Fields of Research (2008): 13 Education > 1301 Education Systems > 130103 Higher Education
13 Education > 1302 Curriculum and Pedagogy > 130208 Mathematics and Numeracy Curriculum and Pedagogy
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/ACCESS.2020.3010938
URI: http://eprints.usq.edu.au/id/eprint/39136

Actions (login required)

View Item Archive Repository Staff Only