We combine Riemannian geometry with the mean field theory of high dimensional chaos to
study the nature of signal propagation in deep neural networks with random weights. Our …

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover
the proficiency level of students on specific knowledge concepts. Existing approaches …

The objective of personalized learning is to design an effective knowledge acquisition track
that matches the learner's strengths and bypasses his/her weaknesses to ultimately meet …

Data-driven methods open up unprecedented possibilities for maritime surveillance using
automatic identification system (AIS) data. In this work, we explore deep learning strategies …

With the rapid development in online education, knowledge tracing (KT) has become a
fundamental problem which traces students' knowledge status and predicts their …

Knowledge tracing (KT), which aims to trace students' changing knowledge state during their
learning process, has improved students' learning efficiency in online learning systems …

We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing
model that separately processes exercise information and student response information …

The growth of online learning, enabled by the availability on the Internet of different forms of
didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases …

Abstract Advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of
Interactive Educational Systems (IESs) have led to the rise of data-driven approaches for …

In Applied AI, or 'machine learning', methods such as neural networks are used to train
computers to perform tasks without human intervention. In this article, we question the …