  Online education has gained an increasing importance over the last decade for
providing affordable high-quality education to students worldwide. This has
been further magnified during the global pandemic as more students switched to
study online. The majority of online education tasks, e.g., course
recommendation, exercise recommendation, or automated evaluation, depends on
tracking students' knowledge progress. This is known as the \emph{Knowledge
Tracing} problem in the literature. Addressing this problem requires collecting
student evaluation data that can reflect their knowledge evolution over time.
In this paper, we propose a new knowledge tracing dataset named Database
Exercises for Knowledge Tracing (DBE-KT22) that is collected from an online
student exercise system in a course taught at the Australian National
University in Australia. We discuss the characteristics of the DBE-KT22 dataset
and contrast it with the existing datasets in the knowledge tracing literature.
Our dataset is available for public access through the Australian Data Archive
platform.

Computers and Society;Artificial Intelligence