A Survey of Knowledge Tracing: Models, Variants, and Applications

TL;DR

This survey comprehensively reviews the progress, models, variants, and applications of Knowledge Tracing in online education, and introduces open-source tools to aid future research and practice.

Contribution

It provides a thorough classification of KT models, reviews recent variants, discusses applications, and offers open-source libraries for datasets and models.

Findings

Identified three fundamental KT model types.

Reviewed numerous variants considering learning assumptions.

Developed open-source libraries EduData and EduKTM.

Abstract

Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge Tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In recent years, a substantial number of studies have concentrated on this rapidly growing field, significantly contributing to its advancements. In this survey, we will conduct a thorough investigation of these progressions. Firstly, we present three types of fundamental KT models with distinct technical routes. Subsequently, we review extensive variants of the fundamental KT models that consider more stringent learning assumptions. Moreover, the development of KT cannot be separated from its applications, thereby we present typical KT applications in various scenarios. To facilitate the work of researchers and practitioners in this field, we have developed two open-source algorithm libraries: EduData that enables the download and preprocessing of KT-related datasets, and EduKTM that provides an extensible and unified implementation of existing mainstream KT models. Finally, we discuss potential directions for future research in this rapidly growing field. We hope that the current survey will assist both researchers and practitioners in fostering the development of KT, thereby benefiting a broader range of students.