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:过去十年来,在线教育在向全世界学生提供负担得起的高质量教育方面越来越重要,随着更多的学生转而在线学习,全球流行病期间,这种教育得到进一步扩大,大多数在线教育任务,例如课程建议、练习建议或自动评价,取决于学生的知识进展,这在文献中被称为“知识追踪”问题。解决这一问题需要收集学生评价数据,反映学生知识的演变情况。在本文中,我们提议建立一个名为“知识追踪数据库练习”(DBE-KT22)的新知识追踪数据集,从澳大利亚国立大学教授的课程中从一个在线学生练习系统收集。我们讨论了DBE-KT22数据集的特点,并将其与知识追踪文献中的现有数据集加以对比。我们的数据集可通过澳大利亚数据档案平台供公众查阅。