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.
Highlights
Over last three decades enormous growth in modelling and computational technologies has occurred, improving dataThe associate editor coordinating the review of this manuscript and approving it for publication was Wei Wang .analytics and computing resources to produce significant innovations [1]
MODELING RESULTS the results generated from artificial intelligence (AI) models (i.e., Extreme Learning Machines (ELM) & random forest (RF)) and the second order Volterra model designed to predict engineering mathematics student performances at University of Southern Queensland (USQ), an Australian regional university, are appraised
The results are used to ascertain whether the optimized ELM model was able to accomplish an acceptable level of accuracy in predicting the weighted score (WS) and the EX, both of which are the key measures used to determine an overall passing grade and the grade point average (GPA) in the program of study
Summary
Over last three decades enormous growth in modelling and computational technologies has occurred, improving data. Analytics and computing resources to produce significant innovations [1]. Many statistical and mathematical modelling tools, including the autoregressive integrated moving average, linear regression and the partial and ordinary differential equations have long been the standard to understand causal inference and the relationships among variables. Deo et al.: Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction data-driven models, focusing on artificial intelligence (AI) have recently been developed and are adopted in a wide range of fields, e.g., education, medical sciences, healthcare, business intelligence, engineering, climate and environmental studies [2]–[5]. Investigators in many of these fields are constantly attempting to employ contemporary AI modelling approaches to develop, evaluate and implement modern-day decision systems
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