Data Mining Techniques Used in Predicting Student Retention in Higher Education: A Survey

Zaid Shuqfa, Saad Harous

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

5 Citations (Scopus)

Abstract

Predicting student retention is a crucial task for all stakeholders in higher education. This paper surveyed the Educational Data Mining (EDM) literature to explore the most recent methods used in building predictive models to predict student's retention, and to foresee the future trends in different context of higher education. We review a diversified set of approaches, models, data sets, tools, techniques, and performance measures. The approaches vary as the educational context varies where opportunities and challenges are associated with each approach. We also present a discussion and a foresight of future directions.

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

  • EDM
  • Educational Data Mining
  • Learning Analytics
  • Prediction
  • Predictive Models
  • Retention
  • Student Success

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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