A Deep Learning Approach Towards Student Performance Prediction in
  Online Courses: Challenges Based on a Global Perspective
Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah
Analyzing and evaluating students' progress in any learning environment is
stressful and time consuming if done using traditional analysis methods. This
is further exasperated by the increasing number of students due to the shift of
focus toward integrating the Internet technologies in education and the focus
of academic institutions on moving toward e-Learning, blended, or online
learning models. As a result, the topic of student performance prediction has
become a vibrant research area in recent years. To address this, machine
learning and data mining techniques have emerged as a viable solution. To that
end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM)
to predict the students' performance at the midpoint stage of the online course
delivery using three distinct datasets collected from three different regions
of the world. Experimental results show that deep learning models have
promising performance as they outperform other optimized traditional ML models
in two of the three considered datasets while also having comparable
performance for the third dataset.