Using Educational Data Mining Techniques to Predict Student Performance

Balqis Al Breiki, Nazar Zaki, Elfadil A. Mohamed

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

9 Citations (Scopus)

Abstract

Educational Data Mining (EDM) involves the extraction of concepts and similar useful information from data sets that store information about academic work. EDM incorporates a toolkit, techniques, and ways of designing research that can automatically reveal correlations and patterns from substantial data sets harvested within educational environments. Making predictions of student attainment has become a significant challenge as educational data sets contain so much data. However, Learning Outcome Assessments (LOA) are crucial both for assessing and effecting improvements in teaching and learning quality and to guide individual students' development. This research aims to make student performance predictions more efficient and accurate, having the aim of offering educational institutions information crucial to improvement of learning outcomes as early as possible. This paper employs regression and several machine learning methods for the development of learning models that can offer accurate predictions of student GPA. Additionally, a number of attribute evaluator methodologies were employed for the identification of those elements that significantly influence a student's total performance.

Original languageEnglish
Title of host publication2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155326
DOIs
Publication statusPublished - Nov 2019
Event2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 - Ras Al Khaimah, United Arab Emirates
Duration: Nov 19 2019Nov 21 2019

Publication series

Name2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Conference

Conference2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period11/19/1911/21/19

Keywords

  • Educational Data Mining
  • attribute selection
  • machine learning classifier
  • regression
  • student performance

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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