Personalized Question Bank Research Based on Particle Swarm Optimization

DOI

10.2991/mbdasm-19.2019.25How to use a DOI?

Keywords

particle swarm optimization; personalized learning; question bank

Abstract

Traditional question banks only pay attention to digitalize questions. This article proposes a new algorithm based on particle swarm optimization improved by personal parameters, which combining analyzing user’s exercise data. This method can provide an online, on-the-fly and personal service for each student. Experiments show that in response to varied numbers of questions, it takes a shorter time and is able to combine user’s understating of themselves knowledge with the right of selecting chapters. This method has the ability to lift students’ experience when using question bank.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhiyun Chen
AU  - Yong Wang
AU  - Yue Bai
PY  - 2019/10
DA  - 2019/10
TI  - Personalized Question Bank Research Based on Particle Swarm Optimization
BT  - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
PB  - Atlantis Press
SP  - 110
EP  - 114
SN  - 2352-538X
UR  - https://doi.org/10.2991/mbdasm-19.2019.25
DO  - 10.2991/mbdasm-19.2019.25
ID  - Chen2019/10
ER  -