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An Adaptive Web Based Educational System Using HMM Approach for C Programming

  • Aditya KhampariaEmail author
  • Babita Pandey
  • Aman Singh
  • Shrasti Tiwari
  • Parampreet Kaur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

The usage of web enabled e-learning systems has been increased for education in recent years. In present study, a Hidden Markov Model (HMM) driven approach is used to predict the future lecture topics or paths of C programming those has been accessed by students in an adaptive web enabled educational system. Data has been preprocessed and collected from e-learning system then HMM parameters were adjusted and used modified algorithm to train the data. This system help faculty to identified the student’s problems and provide assistance to them as per their need. The experiment result shows the accuracy of prediction in proposed system is 80.23% which is better than neural network multilayer perceptron model whose accuracy rate is 78.15%.

Keywords

Prediction E-learning HMM Perceptron Questionnaire 

References

  1. 1.
    Fok, A., Wong, H.S., Chen, Y.S.: Hidden Markov model based characterization of content access patterns in an E-learning environment. In: IEEE International conference on Multimedia and Expo, pp. 201–204 (2005)Google Scholar
  2. 2.
    Ping, J., Jim, K., Beverly, W.: Student behavioral model based prefetching in online tutoring. University of Massachusetts, Technical report, pp. 01–27 (2001)Google Scholar
  3. 3.
    Birney, E.: Hidden Markov model in biological sequence analysis. IBM J. Res. Dev. 45, 834–846 (2001)CrossRefGoogle Scholar
  4. 4.
    Hassan, M.R., Nath, B.: Stock market forecasting using hidden Markov model: a new approach. In: Proceedings of 5th International Conference on Intelligent Systems Design and Applications, pp. 192–196 (2005)Google Scholar
  5. 5.
    Homsi, M., Lutfi, R., Carro, R., Ghias, B.: A hidden Markov model approach to predict student actions in an adaptive and intelligent web based educational system. In: IEEE Xplore, pp. 236–245 (2007)Google Scholar
  6. 6.
    Huang, X., Yong, J., Li, J., Gao, J.: Prediction of student actions using weighted hidden Markov model. In: IT in Medicine and Education, IEEE International Symposium on Digital Object Identifier (2008)Google Scholar
  7. 7.
    Anari, M.S., Anari, S.: Intelligent e-learning systems using student behavior prediction. J. Basic Appl. Sci. 8(3), 12017–12023 (2012)Google Scholar
  8. 8.
    Deeb, B., Hassan, Z., Beseiso, M.: An adaptive HMM based approach for improving e-learning methods. IEEE Proceedings, pp. 456–468 (2014)Google Scholar
  9. 9.
    Wang, Y., Tseng, M.H., Lia, H.C.: Data mining for adaptive learning in a TESL based e-learning system. Expert Syst. Appl. 38, 6480–6485 (2011)CrossRefGoogle Scholar
  10. 10.
    Wang, T.H.: Developing web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-learning environment. Comput. Educ. 57(2), 1801–1812 (2011)CrossRefGoogle Scholar
  11. 11.
    Seters, J.R., Ossevoort, M.A., Tramper, J., Goedhart, M.J.: The influence of student characteristics on the use of adaptive e-learning material. Comput. Educ. 58, 942–952 (2012)CrossRefGoogle Scholar
  12. 12.
    Pandey, B., Mishra, R.B., Khamparia, A.: CBR based approach for adaptive learning in e-learning system. In: IEEE conference on Computer Science and Engineering (Asia Pacific World Conference, Fiji), pp. 1–6 (2014).  https://doi.org/10.1109/apwccse.2014.7053877
  13. 13.
    Tseng, S.S., Su, J.M., Hwang, G.J., Tsai, C.J.: An object oriented course framework for developing adaptive learning systems. Educ. Technol. Soc. 11(2), 171–191 (2008)Google Scholar
  14. 14.
    Chookaew, S., Panjaburee, P., Wanichsan, D., Laosinchai, P.: A personalized e-learning environment to promote students conceptual learning on basic computer programming. Procedia – Soc. Behav. Sci. 116, 815–819 (2013)CrossRefGoogle Scholar
  15. 15.
    Hsieh, T.-C., Wang, T.-I.: A mining based approach on discovering courses pattern for constructing suitable learning path. Expert Syst. Appl. 37(6), 4156–4157 (2010)CrossRefGoogle Scholar
  16. 16.
    Chang, T.Y., Chen, Y.T.: Cooperative learning in e-learning: a peer assessment of student-centered using consistent fuzzy preference. Expert Syst. Appl. 36(4), 8342–8349 (2009)CrossRefGoogle Scholar
  17. 17.
    Yarandi, M., Tawil, H., Rehman, A.: A personalized adaptive e-learning approach based on semantic web technology. Webology 2, 243–256 (2013)Google Scholar
  18. 18.
    Dayhoff, J.E.: Neural Network Architecture. Van Nostrand, New York (1990)Google Scholar
  19. 19.
    Kristofic, A., Bielikova, M.: Improving adaptation in web based educational hypermedia by means of knowledge discovery. In: Proceedings of Sixteenth ACM Conference on Hypertext and Media (2005)Google Scholar
  20. 20.
    Khamparia, A., Pandey, B.: Knowledge and intelligent computing methods in e-learning. Int. J. Technol. Enhanc. Learn. 7(3), 221–242 (2015)CrossRefGoogle Scholar
  21. 21.
    Khamparia, A., Pandey, B.: A novel method of case representation and retrieval in CBR for e-learning. Educ. Inf. Technol. 22, 337–354 (2015).  https://doi.org/10.1007/s10639-015-9447-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Aditya Khamparia
    • 1
  • Babita Pandey
    • 1
  • Aman Singh
    • 1
  • Shrasti Tiwari
    • 1
  • Parampreet Kaur
    • 1
  1. 1.Lovely Professional UniversityPhagwaraIndia

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