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Research on Auto-Generating Test-Paper Model Based on Spatial-Temporal Clustering Analysis

  • Yuling Fan
  • Likai Dong
  • Xuesong Sun
  • Dong Wang
  • Wang Qin
  • Cao Aizeng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

In the process of auto-generating test-paper, the category and the difficulty of the title plays a key role in the quality of generating test-paper. It will produce low quality questions and hard to popularize when used the methods of artificial generating test-paper and random generating test-paper, because considering less on the knowledge point classification and difficulty in the subject. To improve the quality of auto-generating test-paper, this paper takes the evaluation data of ACM Online Judge system as the research object. After normalization, (1) we can get three different results by the K-means clustering analysis based on the temporal and spatial characteristics of time variance and average time; (2) On the basis of clustering, the difficulty index of each topic of all the categories is calculated by using the number of submissions and the number of submissions to solve the problem. The ratio of the two is proportional to the difficulty of the problem. In this paper, the ratio of the two to determine the degree of difficulty index; (3) The Gaussian stochastic process is used to extract numbers of questions of each knowledge point, and calculated the difficulty index which were extracted to make sure they are within range to complete the auto-generating test-paper. In the experiment, we try to train and test the automatic test paper model by the number of professional problems (about 50000 data) in the C language test question of the university OJ system. The average difficulty index of the test paper was 0.4663, which meet the requirements, and the difficulty index of the title fit in with the normal distribution. Compared with the traditional generating test-paper method, the automatic test paper model is based on the difficulty and discrimination of the subject, and it can evaluate the level of tester scientifically. The experimental results show that the proposed automatic test model is simple and effective.

Keywords

Spatial-temporal feature Clustering algorithm Difficult coefficient Auto-generating test paper 

Notes

Acknowledgments

This research was supported by Shandong Provincial Natural Science Foundation (No. ZR2018LF005), Industry-University Cooperative Education Project of Ministry of Education (No. 201601023018), the Scientific Research Fund of Jinan University (No. XKY1711, No. XKY1622, No. XBS1653) and Teaching Research Project of Jinan University (No. J1638).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuling Fan
    • 1
  • Likai Dong
    • 1
  • Xuesong Sun
    • 1
  • Dong Wang
    • 1
    • 2
  • Wang Qin
    • 1
  • Cao Aizeng
    • 1
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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