Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
نویسندگان
چکیده
Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, prediction of students’ final course grades with continuous assessment marks is a useful toolkit degree program educators. Predictive models are practical tools used to evaluate effectiveness as well assessing progression and implementing interventions best learning outcomes. This study develops novel multivariate adaptive regression spline (MARS) model predict weighted score WS (i.e., grade). To construct proposed MARS model, performance data over five years from University Southern Queensland, Australia, were design predictive using input predictors online quizzes, written assignments, examination scores. About 60% randomised predictor grade applied train (with 25% training set validation) 40% test model. Based on cross-correlation inputs vs. WS, 12 distinct combinations single M1–M5) multiple (M6–M12) features created assess influence each results bench-marked via decision tree (DTR), kernel ridge (KRR), k-nearest neighbour (KNN) The clearly showed that quizzes provide least contribution. However, improved dramatically by including assignments research demonstrates merits in uncovering relationships among variables, which also provides advantage educators early intervention moderating their predicting students ahead outcome course. findings future application have significant implications or planning aimed improve graduate outcomes undergraduate engineering cohorts.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su141711070