Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Leila Ismail, Huned Materwala, Alain Hennebelle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions.

Original languageEnglish
Title of host publicationAdvances in Digital Science - ICADS 2021
EditorsTatiana Antipova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030717810
Publication statusPublished - 2021
EventInternational Conference on Advances in Digital Science, ICADS 2021 - Salvador, Brazil
Duration: Feb 19 2021Feb 21 2021

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Advances in Digital Science, ICADS 2021


  • Artificial intelligence
  • Classification models
  • Educational data mining
  • Educational machine learning
  • Student performance prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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