Clickstream Knowledge Tracing: Modeling How Students Answer Interactive Online Questions

Wai-Lun Chan, Dit-Yan Yeung

Abstract: Knowledge tracing (KT) is a research topic which seeks to model the knowledge acquisition process of students by analyzing their past performance in answering questions, based on which their performance in answering future questions is predicted. However, existing KT models only consider whether a student answers a question correctly when the answer is submitted but not the in-question activities. We argue that the interaction involved in the in-question activities can at least partially reveal the thinking process of the student, and hopefully even the competence of acquiring or understanding each piece of the knowledge required for the question. Based on real student interaction clickstream data collected from an online learning platform on which students solve mathematics problems, we conduct clustering analysis for each question to show that clickstreams can reflect different student behaviors. We then propose the first clickstream-based KT model, dubbed clickstream knowledge tracing (CKT), which augments a basic KT model by modeling the clickstream activities of students when answering questions. We apply different variants of CKT and compare them with the baseline KT model which does not use clickstream data. Despite the limited number of questions with clickstream data and its noisy nature which may compromise the data quality, we show that incorporating clickstream data leads to performance improvement. Through this pilot study, we hope to open a new direction in KT research to analyze finer-grained interaction data of students on online learning platforms.