Advertisement

A Novel Genetic Algorithm for Test Sheet Assembling Problem in Learning Cloud

  • Shih-Pang TsengEmail author
  • Long-Yeu Chung
  • Po-Lin Huang
  • Ming-Chao Chiang
  • Chu-Sing Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

The assessment is the most effectively tool for the teachers to realized the learning status of the learners. The test sheet assembling is an important job in the E-learning. In the future learning cloud environment, the large amount of items would be aggregated into the itembank from various sources. The test sheet assembling algorithm should be with the ability of abstract the needed information directly from the items. This paper proposed an effective method based on genetic algorithm to solve the test sheet assembling problem. The experimental result shows the effectiveness of the proposed method.

Keywords

E-learning Genetic algorithm Test sheet assembling 

Notes

Acknowledgments

The authors would also like to thank Kang Hsuan Publishing and Han Lin Publishing for providing their itembanks and the Chinese Knowledge and Information Processing (CKIP) group, Institute of Information Science, Academia Sinica for providing their Chinese Word Segmentation System to support this research.

References

  1. 1.
    Zhang D, Zhao JL, Zhou L, Nunamaker JF Jr (2004) Can e-learning replace classroom learning? Commun ACM 47(5):75–79CrossRefGoogle Scholar
  2. 2.
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRefGoogle Scholar
  3. 3.
    Tseng SP, Chiang MC, Yang CS, Tsai CW (2010) An efficient algorithm for integrating heterogeneous itembanks. Int J Innovative Comput, Inf Control 6(10):4319–4334Google Scholar
  4. 4.
    Bork A, Gunnarsdottir S (2001) Individualization and interaction. Tutorial Distance Learning, vol 12 of Innovations in Science Education and Technology. Springer, Netherlands, pp 47–62Google Scholar
  5. 5.
    Pellegrino JW, Chudowsky N, Glaser R (2001) Knowing what students know: the science and design of educational assessment. The National Academies Press, USAGoogle Scholar
  6. 6.
    Chua YP (2012) Effects of computer-based testing on test performance and testing motivation. Comput Hum Behav 28(5):1580–1586MathSciNetCrossRefGoogle Scholar
  7. 7.
    Llamas-Nistal M, Fernndez-Iglesias MJ, Gonzlez-Tato J, Mikic-Fonte FA (2013) Blended e-assessment: migrating classical exams to the digital world. Comput Educ 62:72–87CrossRefGoogle Scholar
  8. 8.
    JISC (2007) Effective practice with e-assessment: an overview of technologies, policies and practice in further and higher educationGoogle Scholar
  9. 9.
    Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308CrossRefGoogle Scholar
  10. 10.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, HeidelbergGoogle Scholar
  12. 12.
    Holland JH (1992) Adaptation in Natural and Artificial Systems. MIT Press, BostonGoogle Scholar
  13. 13.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. pp 1942–1948Google Scholar
  14. 14.
    Hwang GJ, Yin PY, Yeh SH (2006) A tabu search approach to generating test sheets for multiple assessment criteria. Education, IEEE Transactions on 49(1):88–97CrossRefGoogle Scholar
  15. 15.
    Gu P, Niu Z, Chen X, Chen W (2011) A personalized genetic algorithm approach for test sheet assembling. In Leung H, Popescu E, Cao Y, Lau R, Nejdl W (eds) Advances in web-based learning—ICWL 2011. Vol 7048 of Lecture notes in computer science, Springer, Berlin, pp 164–173Google Scholar
  16. 16.
    Chinese Document Segmentation (2008). http://ckipsvr.iis.sinica.edu.tw/
  17. 17.
    Porter MF (1997) Readings in information retrieval. Morgan Kaufmann Publishers Inc., San Francisco, pp 313–316Google Scholar
  18. 18.
    Miller BL, Miller BL, Goldberg DE, Goldberg DE (1995) Genetic algorithms, tournament selection, and the effects of noise. Complex Systems 9:193–212MathSciNetGoogle Scholar
  19. 19.
    Han Lin Publishing (2002). http://www.hle.com.tw/
  20. 20.
    Kang Hsuan Publishing (2002). http://www.knsh.com.tw/

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Shih-Pang Tseng
    • 1
    • 5
    Email author
  • Long-Yeu Chung
    • 2
  • Po-Lin Huang
    • 3
  • Ming-Chao Chiang
    • 1
  • Chu-Sing Yang
    • 4
  1. 1.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, Republic of China
  2. 2.Department of Applied Informatics and MultimediaChia Nan University of Pharmacy and ScienceTainanTaiwan, Republic of China
  3. 3.Center of General EducationKao Yuan UniversityKaohsiungTaiwan, Republic of China
  4. 4.Department of Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan, Republic of China
  5. 5.Department of Computer Science and Information EngineeringTajen UniversityPingtungTaiwan, Republic of China

Personalised recommendations