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Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model2.42 MB
Published VersionCC BY V4.0, Open Access
Journal article
Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
Sustainability, Vol.14(17), pp.1-27
2022
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.
Details
- Title
- Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
- Authors
- Abul Abrar Masrur Ahmed (Author) - University of Southern QueenslandRavinesh C. Deo (Corresponding Author) - University of Southern QueenslandSujan Ghimire (Author) - University of Southern QueenslandNathan J. Downs (Author) - UniSQ’s Advanced Data Analytics Research Group, School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD 4300, AustraliaAruna Devi (Author) - University of the Sunshine Coast, Queensland, School of Education and Tertiary AccessPrabal D. Barua (Author) - University of Southern QueenslandZaher M. Yaseen (Author) - University of Southern Queensland
- Publication details
- Sustainability, Vol.14(17), pp.1-27
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/su141711070
- ISSN
- 2071-1050
- Copyright note
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Grant note
- UniSQ through School of Sciences Quartile 1 Challenge Grant (2018) UniSQ Excellence in Research 2021 grant
- Organisation Unit
- Indigenous and Transcultural Research Centre; School of Education and Tertiary Access; University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99677498602621
- Output Type
- Journal article
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