In this era when every aspect of society is accelerating, people are always seeking
improvement to stay competitive in their careers. E-learning systems fit into the ever …

Graph-based recommendation approaches use a graph model to represent the
relationships between users and items, and exploit the graph structure to make …

The prevalence of e-learning systems and on-line courses has made educational material
widely accessible to students of varying abilities and backgrounds. There is thus a growing …

Data sparsity, that is a common problem in neighbor-based collaborative filtering domain,
usually complicates the process of item recommendation. This problem is more serious in …

During the last decade a huge amount of data have been shown and introduced in the
Internet. Recommender systems are thus predicting the rating that a user would give to an …

We address the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational approach to this …

To provide intelligent learning guidance for students in e-learning systems, it is necessary to
accurately predict their performance in future exams by analyzing score data in past exams …

Traditionally, the task difficulty level is often determined by domain experts based on some
hand-crafted rules. However, with the adoption of Massive Open Online Courses (MOOCs) …

During the recent epidemic period of COVID‐19, online courses have become an important
learning form for college students. However, online learning cannot communicate face to …

It is a fundamental function of a personalized elearning system to recommend suitable
exercises to learners for improving their learning efficiencies and qualities. These exercises …