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

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

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

 

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

IEEE

IEEE International Conference on Advances in Computing and Communication Engineering - Las Vegas, United States

22 Jun 2020  24 Jun 2020

Cite: Al-Tameemi, G, Xue, J, Ajit, S, Kanakis, T & Hadi, I 2020, Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges. in Predictive Learning Analytics in Higher Education: Factors, Methods and Challenges. IEEE, 6th IEEE International Conference on Advances in Computing and Communication Engineering, Las Vegas, United States, 22/06/20.

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