Abstract—Auto-Generated Test Paper (AGTP) has been
deeply studied for many years, however, it is still a difficult
problem and the certainty to access the best test paper (TP) is
not guaranteed yet. In this paper, we put forward a method for
AGTP based on knowledge embedding, which makes AGTP
easier and faster. The knowledge to be embedded is studied and
the mechanism behind it is analyzed. The embedded knowledge
in this paper is from both the constraints of TP and the
information of question repository (QR). The experiments
validated the proposed method and found it is not only faster but
also costs less computational resources to access the best TP than
other method, such as evolutionary algorithm. What impressed
is that the cost time to access the optimum does not rapidly
increase with the size of QR. The knowledge plays the important
role in AGTP, especially to efficiently improve the performance
of the algorithms.
Index Terms—Auto-generated test paper, evolutionary algorithm, knowledge, population initiation.
Duan Zhen-Hui, Hao Guo-Sheng, He Xiao-Dan, and He Yi-Yang are with the School of Computer Science & Technology, Jiangsu Normal University, Xuzhou, 221000, China (e-mail: email@example.com).
Hu Xing-Liu is with the College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, 211169, China (e-mail: firstname.lastname@example.org).
Luo Fang is with the School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China (e-mail: email@example.com).
Cite: Duan Zeng-Hui, Hu Xing-Liu, Hao Guo-Sheng, Luo Fang, He Xiao-Dan, and He Yi-Yang, "Auto-generated Test Paper Based on Knowledge Embedding," International Journal of Information and Education Technology vol. 9, no. 9, pp. 671-677, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).