Online Test Paper Composition Based on Genetic Algorithm
- 10.2991/msam-18.2018.34How to use a DOI?
- test paper composition; education intellectualization; genetic algorithm; E-learning
The application of online examination is used more and more widely. Test paper composition is the core function in this application. Online examination system requires that the test paper should be quick and flexible, and the composition of questions should be reasonable and random. Test paper should not only meet the specific needs of users, ensure fairness, impartiality, but also be accurate and efficient. The intelligent test paper composition algorithm based on genetic algorithm is studied in this paper. Then this method is used to organize the structure of the test paper automatically, composites the examination content. Proposals are put forward to examines the degree of students' mastery of knowledge based on this method. This algorithm can automatically composite test papers according to the conditions of difficulty degree, knowledge coverage, and the proportion of questions. It can automatically composite test papers for online assessment. The practice of online testing system based on this algorithm shows that the algorithm is scientific and effective. On the premise of guaranteeing the quality of the test paper, it greatly improves the efficiency and pertinence of online testing. It is beneficial to improve students' learning efficiency and intellectualization of education.
- © 2018, 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/).
Cite this article
TY - CONF AU - Wanli Song PY - 2018/07 DA - 2018/07 TI - Online Test Paper Composition Based on Genetic Algorithm BT - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018) PB - Atlantis Press SP - 158 EP - 161 SN - 1951-6851 UR - https://doi.org/10.2991/msam-18.2018.34 DO - 10.2991/msam-18.2018.34 ID - Song2018/07 ER -