  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.

Computers and Society;Machine Learning