Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model

Ahmed, Abul Abrar Masrur and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Ghimire, Sujan and Downs, Nathan J. ORCID: https://orcid.org/0000-0002-3191-6404 and Devi, Aruna and Barua, Prabal D. ORCID: https://orcid.org/0000-0001-5117-8333 and Yaseen, Zaher M. (2022) Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model. Sustainability, 14 (17):11070. pp. 1-27.

[img]
Preview
Text (Published Version)
sustainability-14-11070-Published Copy.pdf
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview

Abstract

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score WS (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the WS, 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the WS with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on WS clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts.


Statistics for USQ ePrint 51099
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 05 Sep 2022 23:26
Last Modified: 23 Sep 2022 02:26
Uncontrolled Keywords: educational decision making; multivariate regression spline model; student performance; artificial intelligence in education; engineering mathematics student performance
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning
39 EDUCATION > 3903 Education systems > 390303 Higher education
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.3390/ su141711070
URI: http://eprints.usq.edu.au/id/eprint/51099

Actions (login required)

View Item Archive Repository Staff Only