Knowledge tracing (KT) is the task of using students’ historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge.

In this talk, we will comprehensively review recent developments of applying state-of-the-art deep learning approaches in KT problems, with a focus on those real-world educational data. Beyond introducing the recent advances of various DLKT models, we will discuss how to guarantee valid comparisons across DLKT methods via thorough evaluations on several publicly available datasets. More specifically, we will talk about (1) KT related psychometric theories; (2) the general DLKT modeling framework that covers recently developed DLKT approaches from different categories; (3) the general DLKT benchmark that allows existing approaches comparable on public KT datasets; (4) the broad application of algorithmic assessment and personalized feedback. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world KT applications.