Tomasevic, N., Gvozdenovic, N. and Vranes, S. (2019) An Overview and Comparison of Supervised Data Mining Techniques for Student Exam Performance Prediction. Computers & Education, 143, Article ID: 103676.
https://doi.org/10.1016/j.compedu.2019.103676

has been cited by the following article:

• TITLE: Students’ Numeracy and Literacy Aptitude Analysis and Prediction Using Machine Learning

  AUTHORS: Tianyu Li

  KEYWORDS: Numeracy and Literacy, Machine Learning, Learning Analytics, Classification

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.8, August 26, 2022

  ABSTRACT: Education is one of the most pivotal services in societal development as it cultivates a wide variety of skills, especially numeracy and literacy skills. However, students may have varying masteries of these two aptitudes. Some attribute this to students’ intrinsic efforts while others attribute this to students’ capabilities and affiliated environments. In this work, I explore the numeracy and literacy aptitude patterns of students from various cultures based on a dataset that contains various demographic information, from which I deduced some preliminary trends. After the comparison of numerous machine learning algorithms, the optimal algorithm or combination of a few algorithms predicts students’ performances by classifying students of different backgrounds into various potential outcomes. The results suggest that proper resources and supports are necessary for enhanced learning.