Mariel Musso

TL;DR: In this article, the authors used cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN).

TL;DR: In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university.

TL;DR: Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score and it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education.

TL;DR: This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs).

TL;DR: This article analyzed the relationship between working memory capacity, executive attention, and self-regulated learning (SRL) on math performance and more specifically on items with different levels of complexity and difficulty and found support for a complex pattern of interactions between cognitive processes and components of SRL model at the strategy level, in their effect on MP, and given specific item characteristics.