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.

翻译：过去十年间，在线教育因能为全球学生提供可负担的高质量教育而日益重要。这一趋势在全球疫情期间进一步凸显，更多学生转向在线学习。大多数在线教育任务（如课程推荐、习题推荐或自动评估）都依赖于追踪学生的知识进展。这在文献中被称为"知识追踪"问题。解决该问题需要收集能够反映学生知识随时间演变的学生评估数据。本文提出一个名为"知识追踪数据库练习集（DBE-KT22）"的新知识追踪数据集，该数据集来自澳大利亚国立大学一门课程的在线学生练习系统。我们讨论了DBE-KT22数据集的特征，并将其与现有知识追踪文献中的数据集进行对比。该数据集通过澳大利亚数据存档平台向公众开放获取。