  With advances in Artificial Intelligence in Education (AIEd) and the
ever-growing scale of Interactive Educational Systems (IESs), data-driven
approach has become a common recipe for various tasks such as knowledge tracing
and learning path recommendation. Unfortunately, collecting real students'
interaction data is often challenging, which results in the lack of public
large-scale benchmark dataset reflecting a wide variety of student behaviors in
modern IESs. Although several datasets, such as ASSISTments, Junyi Academy,
Synthetic and STATICS, are publicly available and widely used, they are not
large enough to leverage the full potential of state-of-the-art data-driven
models and limits the recorded behaviors to question-solving activities. To
this end, we introduce EdNet, a large-scale hierarchical dataset of diverse
student activities collected by Santa, a multi-platform self-study solution
equipped with artificial intelligence tutoring system. EdNet contains
131,441,538 interactions from 784,309 students collected over more than 2
years, which is the largest among the ITS datasets released to the public so
far. Unlike existing datasets, EdNet provides a wide variety of student actions
ranging from question-solving to lecture consumption and item purchasing. Also,
EdNet has a hierarchical structure where the student actions are divided into 4
different levels of abstractions. The features of EdNet are domain-agnostic,
allowing EdNet to be extended to different domains easily. The dataset is
publicly released under Creative Commons Attribution-NonCommercial 4.0
International license for research purposes. We plan to host challenges in
multiple AIEd tasks with EdNet to provide a common ground for the fair
comparison between different state of the art models and encourage the
development of practical and effective methods.
