Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

By Shaobo Huang, Ning Fang. Comput. Educ., 2013

368 citations recorded by the source corpus.

Subfields: Computer Science Applications, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/388a5acf4d701ac8ba4b55985702aea9c3ae4ffb

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In Computer Science Applications

In Statistics and Probability

Citations in the corpus

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review (2020, methodology): “Pursuing this goal, four mathematical models were compared to predict students’ performance in a basic course, a high-impact course and a high-enrollment course in engineering dynamics [16].”
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, background): “Prediction of performance Adding more predictor variables does not help improve the average prediction accuracy of the mathematical models explored by Huang and Fang (2013) for prediction of performance.”
Analyzing undergraduate students' performance using educational data mining (2017, background): “…solving skills, gender, prior experience, high school mathematics grade, locality, previous computer programming experience, and e-learning usage; Huang and Fang (2013) predict course performance on the basis of students' performance in prerequisite courses and midterm examinations; Romero,…”
Models for early prediction of at-risk students in a course using standards-based grading (2016, methodology): “For example, to predict grades Huang and Fang (2012) used multiple linear regression (MLR), multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, and support vector machine (SVM) (see section 3.3 or (Hand, Mannila, & Smyth, 2001) for more details about these…”
Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, background): “However, when the focus is on forecasting, neural networks (Delen, 2010; Vandamme et al., 2007), support vector machines (Huang & Fang, 2013), decision trees (Delen, 2011; Nandeshwar et al., 2011) and random forests (Delen, 2010; Vandamme et al., 2007) available input data.”