In this section
@article{Cully:2019:10.1109/TKDE.2019.2912367,
author = {Cully, A and Demiris, Y},
doi = {10.1109/TKDE.2019.2912367},
journal = {IEEE Transactions on Knowledge and Data Engineering},
pages = {2000--2013},
title = {Online knowledge level tracking with data-driven student models and collaborative filtering},
url = {http://dx.doi.org/10.1109/TKDE.2019.2912367},
volume = {32},
year = {2019}
}
TY - JOUR
AB - Intelligent Tutoring Systems are promising tools for delivering optimal and personalised learning experiences to students. A key component for their personalisation is the student model, which infers the knowledge level of the students to balance the difficulty of the exercises. While important advances have been achieved, several challenges remain. In particular, the models should be able to track in real-time the evolution of the students' knowledge levels. These evolutions are likely to follow different profiles for each student, while measuring the exact knowledge level remains difficult given the limited and noisy information provided by the interactions. This paper introduces a novel model that addresses these challenges with three contributions: 1) the model relies on Gaussian Processes to track online the evolution of the student's knowledge level over time, 2) it uses collaborative filtering to rapidly provide long-term predictions by leveraging the information from previous users, and 3) it automatically generates abstract representations of knowledge components via automatic relevance determination of covariance matrices. The model is evaluated on three datasets, including real users. The results demonstrate that the model converges to accurate predictions in average 4 times faster than the compared methods.
AU - Cully,A
AU - Demiris,Y
DO - 10.1109/TKDE.2019.2912367
EP - 2013
PY - 2019///
SN - 1041-4347
SP - 2000
TI - Online knowledge level tracking with data-driven student models and collaborative filtering
T2 - IEEE Transactions on Knowledge and Data Engineering
UR - http://dx.doi.org/10.1109/TKDE.2019.2912367
UR - https://ieeexplore.ieee.org/document/8697129
UR - http://hdl.handle.net/10044/1/70239
VL - 32
ER -
