Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges

Ghaith Al-Tameemi*, James Xue, Suraj Ajit, Triantafyllos Kanakis, Israa Hadi

*Corresponding author for this work

Research output: Contribution to Book/ReportConference Contribution

Abstract

In higher education institutions, a high number of studies show that the use of predictive learning analytics can positively impact student retention and the other aspects which lead to student success. Predictive learning analytics examines the learning data for intervening or improving the process itself that positively reflects on student performance. In our survey, we are considering the most recent research papers focusing on predictive learning analytics and how that affects the final student outcome in educational institutions. The process of predictive learning analytics, such as data collection, data preprocessing, data mining, and others, has been illustrated in detail. We have identified factors that affect student performance. Several machine learning approaches have also been compared to provide a clear view of the most suitable algorithms and tools used for implementing the learning analytics
Original languageEnglish
Title of host publicationPredictive Learning Analytics in Higher Education: Factors, Methods and Challenges
PublisherIEEE
Publication statusAccepted/In press - 5 May 2020
Event6th IEEE International Conference on Advances in Computing and Communication Engineering - Las Vegas, United States
Duration: 22 Jun 202024 Jun 2020

Conference

Conference6th IEEE International Conference on Advances in Computing and Communication Engineering
Abbreviated titleICACCE
CountryUnited States
CityLas Vegas
Period22/06/2024/06/20

Keywords

  • Predictive Learning Analytics
  • Educational Data Mining
  • Higher education institutions
  • Data mining
  • Student performance

Fingerprint Dive into the research topics of 'Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges'. Together they form a unique fingerprint.

  • Cite this

    Al-Tameemi, G., Xue, J., Ajit, S., Kanakis, T., & Hadi, I. (Accepted/In press). Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges. In Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges IEEE.