Knowledge tracing: A bibliometric analysis

With the advent of artificial intelligence, most of the main techniques have found their way into intelligent education. Knowledge tracing is one of the essential tasks in educational research, which aims to model and qualify students' procedural knowledge acquisition using machine learning or deep learning techniques. While numerous studies have focused on improving models and algorithms of knowledge tracing, few have thoroughly examined the dynamic and complex aspects of this research field. This study conducts a bibliometric analysis that included 383 key articles published between 1992 and 2021 to review the evolutionary nuances of knowledge tracing research. Besides, we employ document clustering to uncover the most common topics of knowledge tracing and systematically review each topic's characteristics. Major findings include broad knowledge tracing trends information such as the most productive authors, the most referenced articles, and the occurrence of author keywords. Existing knowledge tracing models are further divided into three clusters: Markov process-based knowledge tracing, logistic knowledge tracing, and deep learning-based knowledge tracing. The attributes of each cluster were then discussed, as well as recent development and application. Finally, we highlighted existing constraints and identified promising future research topics in knowledge tracing.