Knowledge Tracing Challenge: Optimal Activity Sequencing for Students

TL;DR

This paper evaluates two knowledge tracing algorithms on a new dataset from the AAAI2023 challenge, aiming to improve personalized education by accurately modeling student learning progress.

Contribution

It introduces the implementation and comparison of two knowledge tracing algorithms on a novel dataset within a competitive challenge setting.

Findings

Algorithms achieved significant accuracy in predicting student knowledge states

The study highlights strengths and limitations of each approach in real-world data

Results suggest potential for enhancing adaptive learning systems

Abstract

Knowledge tracing is a method used in education to assess and track the acquisition of knowledge by individual learners. It involves using a variety of techniques, such as quizzes, tests, and other forms of assessment, to determine what a learner knows and does not know about a particular subject. The goal of knowledge tracing is to identify gaps in understanding and provide targeted instruction to help learners improve their understanding and retention of material. This can be particularly useful in situations where learners are working at their own pace, such as in online learning environments. By providing regular feedback and adjusting instruction based on individual needs, knowledge tracing can help learners make more efficient progress and achieve better outcomes. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials recommendations. In this paper, we will present the results of the implementation of two Knowledge Tracing algorithms on a newly released dataset as part of the AAAI2023 Global Knowledge Tracing Challenge.