Online learning has become increasingly important, having in mind the latest events,
imposed isolation measures and closed schools and campuses. Consequently, teachers …

There is a growing interest in developing and implementing adaptive instructional systems
to improve, automate, and personalize student education. A necessary part of any such …

Scientifically and effectively tracking student knowledge states is a significant and
fundamental task in personalized education. Many neural network-based models, eg, deep …

Abstract As modern Artificial Intelligence (AI) techniques continue to penetrate many aspects
of our lives, there is growing interest in the adoption of AI in education and how learning can …

Student performance is affected by their knowledge which changes dynamically over time.
Therefore, employing recurrent neural networks (RNN), which are known to be very good in …

Across intelligent education, knowledge tracing (KT) is a fundamental problem in realizing
personalized education. Recently, several approaches using Recurrent Neural Networks …

Abstract Knowledge tracing refers to the dynamic assessment of a learner's mastery of skills.
There has been widespread adoption of the self-attention mechanism in knowledge-tracing …

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to
predict the correctness of students' responses to questions using historical clickstream data …

Introduction Frequent formative assessment is essential for accurately evaluating student
learning, enhancing engagement, and providing personalized feedback. In STEM …

Abstract Knowledge tracing (KT) is a technique that can be applied to predict students'
current skill mastery levels and future academic performance based on previous question …