This paper shows how web usage mining can be applied in e‐learning systems in order to
predict the marks that university students will obtain in the final exam of a course. We have …

An affective state of a learner in E-learning has gained enormous interest. The prediction of
the emotional state of a learner can enhance the outcome of learning by including …

Applying artificial intelligence (AI) to support science learning is a prominent aspect of the
digital education revolution. This study investigates students' interaction and learning with …

Learning environments aim to deliver efficacious instruction, but rarely take into
consideration the motivational factors involved in the learning process. However …

Academic motivation is closely related to academic performance. For educators, it is equally
important to detect early students with a lack of academic motivation as it is to detect those …

Most e-Learning systems store data about the learner's actions in log files, which give us
detailed information about learner behaviour. Data mining and machine learning techniques …

We present text replays, a method for generating labels that can be used to train classifiers
of student behavior. We use this method to label data as to whether students are gaming the …

Online learning has become increasingly popular due to technology advancement that
allows discussion to occur at distance. Most studies report on students' learning …

Feedback plays an important role in self-regulated learning. However, little is known about
how students' feedback perception affects their self-regulation process in learning. This …

Engagement is an important aspect of effective learning. Time spent using an e-Learning
system is not quality time if the learner is not engaged. Tracking the student disengagement …