@inproceedings{a1d0645e61694690b654de36d0158f8f,
title = "Genetic Algorithm for Bayesian Knowledge Tracing: A Practical Application",
abstract = "Online intelligent tutoring systems have developed rapidly in recent years. Analyzing educational data to help students personalize learning has become a research hotspot. Knowledge Tracing (KT) aims to assess students{\textquoteright} changing cognitive states of skills by analyzing their performance on answers. As a representative KT model, Bayesian Knowledge Tracing (BKT) has good interpretability due to the use of the Hidden Markov Model. However, BKT needs to model students{\textquoteright} performance on different skills separately. If BKT simultaneously traces the cognitive states of students{\textquoteright} multiple skills, its time complexity increases exponentially with the number of skills. Therefore, we introduce a genetic algorithm to solve this problem and propose a Multi-skills BKT. This approach allows the BKT model to handle multiple skills simultaneously. Experiments on real datasets show that the model has a significant improvement in prediction performance over the BKT.",
keywords = "Bayesian knowledge tracing, Genetic algorithm, Multiple knowledge skills",
author = "Shuai Sun and Xuegang Hu and Chenyang Bu and Fei Liu and Yuhong Zhang and Wenjian Luo",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Conference on Swarm Intelligence, ICSI 2022 ; Conference date: 15-07-2022 Through 19-07-2022",
year = "2022",
doi = "10.1007/978-3-031-09677-8\_24",
language = "英语",
isbn = "9783031096761",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "282--293",
editor = "Ying Tan and Yuhui Shi and Ben Niu",
booktitle = "Advances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part I",
address = "德国",
}