Skip to main content

Advertisement

Springer Nature Link
Log in

Discrete model based answer script evaluation using decision tree rule classifier

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Student answer script contains different type of answers to be evaluated. The answers may be of objective type answers, subjective type answers, mathematical answers, diagrammatic answers, classification type answers, each of which may require unique approaches for automated evaluation. Classification type answer makes a student to map an answer/object to a particular class/type, like types of grammars, types of normal forms and types of functions. This paper proposes an approach to automate the classification type answers using discrete model which classifies the types of relations in discrete mathematics domain. The proposed approach achieves 100% classification accuracy when compared with other types of classification since the pellet reasoner is used as a classifier which uses predefined classification rules to classify the types of relations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Awan, T.H., Sandnes, F.E.: A tool for automated continuous assessment in numerical methods courses feedback. Int. Conf. Eng. Educ. 21(22), 1–5 (2002)

    Google Scholar 

  2. Yang, J., Li, J., Liu, S.: A novel technique applied to the economic investigation of recommender system. Multimed. Tools Appl. https://doi.org/10.1007/s11042-017-4752-4 (2017)

    Article  Google Scholar 

  3. Xu, Q., Wu, J., Chen, Q.: A novel mobile personalized recommended method based on money flow model for stock exchange. Math. Probl. Eng. (2014) https://doi.org/10.1155/2014/353910

    MathSciNet  MATH  Google Scholar 

  4. Xu, Q.: A novel machine learning strategy based on two-dimensional numerical models in financial engineering. Math. Probl. Eng. (2013) https://doi.org/10.1155/2013/659809

    Google Scholar 

  5. Li, G., Zhang, Z., Wang, L., Chen, Q., Pan, J.: One-class collaborative filtering based on rating prediction and ranking prediction. Knowl.-Based Syst. 124, 46–54 (2017)

    Article  Google Scholar 

  6. Gai, L.: Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing 204, 17–25 (2016)

    Article  Google Scholar 

  7. Palaniappan, S., Krishnamurthi, I.: Register linear based model for question classification using costa level questions. WSEAS Trans. Comput. 14, 369–381 (2015)

  8. Bahmani, A.H., Naghibzadeh, M., Bahmani, B.: Automatic database normalization and primary key generation. In: CCECE, Canadian Conference on Electrical and Computer Engineering, pp. 11–16. IEEE (2008)

  9. Ramamurthy, M., Krishnamurthi, I.: Decision tree based classification type question/answer e-assessment system. Adv. Nat. Appl. Sci. 10(1), 22–25 (2016)

    Google Scholar 

  10. Heumann, B.W.: An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. J. Remote Sens. 3, 2440–2460 (2011)

    Article  Google Scholar 

  11. Santoro, K., Bilisoly, R.: Creating, automating, and assessing online homework in introductory statistics and mathematics classes. JSM 2014-Section on Statistical Education, pp. 1787–1798 (2015)

  12. Su, C.Y., Wang, L.: Construction and analysis of educational assessments using knowledge maps with weight appraisal of concepts. Comput. Educ. 55(3), 1300–1311 (2010)

    Article  Google Scholar 

  13. Richter-Gebert, J., Kortenkamp, U.H.: Interactive Geometry Software’. Cinderella. Version 1.2. User Manual for the Interactive Geometry Software Cinderella. Springer Science & Business Media. Springer, Heidelberg (1999)

  14. Nguyen, M.L., Hui, S.C., Fong, A.C.M.: Probabilistic equivalence verification approach for automatic mathematical solution assessment. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2028–2034 (2013)

  15. Nosofsky, R.M.: Relations between exemplar-similarity and likelihood models of classification. J. Math. Psychol. 34(4), 393–418 (1990)

    Article  Google Scholar 

  16. Ilango, S. et.al.: Optimization using artificial bee colony based clustering approach for big data. Cluster Comput. (2018) https://doi.org/10.1007/s10586-017-1571-3

    Article  Google Scholar 

  17. Lehner, W., Albrecht, J., Wedekind, H.: Normal forms for multidimensional databases. In: Proceedings of Tenth International Conference on Scientific and Statistical Database Management, pp. 63–72. IEEE (1998)

  18. Cai, Y., Cercone, N., Han, J.: An attribute-oriented approach for learning classification rules from relational databases. In: Proceedings on sixth international conference on Data Engineering, pp. 281–288 (1990)

  19. Nabi, S.A., Rasool, S., Premchand, P.: Detection and extraction of videos using decision trees. Int. J. Adv. Comput. Sci. Appl. 2(12), 147–151 (2011)

    Google Scholar 

  20. Hui, Y., Rongqun, Z., Li, X.: Extracting wetland using decision tree classification. In: WSEAS International Conference Proceedings Mathematics and Computers in Science and Engineering, World Scientific and Engineering Academy and Society, vol. 8, pp. 240–245 (2009)

  21. Sugumaran, V., Muralidharan, V., Ramachandran, K.I.: Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process. 22(2), 930–942 (2002)

    Article  Google Scholar 

  22. Fan, C.Y., Chang, P.C., Lin, J.J., Hsieh, J.C.: A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11(1), 632–644 (2011)

    Article  Google Scholar 

  23. Exarchos, T.P., Tsipouras, M.G., Exarchos, C.P., Papaloukas, C., Fotiadis, D.I., Michalis, L.K.: A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 40(3), 187–200 (2007)

    Article  Google Scholar 

  24. Geurts, P., Fillet, M., De Seny, D., Meuwis, M.A., Malaise, M., Merville, M., P, Wehenkel, L.: Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics 21(14), 3138–3145 (2005)

    Article  Google Scholar 

  25. Vogrinčič, S., Bosnić, Z.: Ontology-based multi-label classification of economic articles. Comput. Sci. Inf. Syst. 8(1), 101–119 (2011)

    Article  Google Scholar 

  26. De Luca, E.W., Nürnberger, A., Von-Guericke, O.: Ontology-based semantic online classification of documents: supporting users in searching the web. In: Proceedings of the European Symposium on Intelligent Technologies (EUNITE), Aachen, pp. 1–9 (2004)

  27. Rajan, R.H., Dhas, J.P.M.: A method for classification based on association rules using ontology in Web data. Int. J. Comput. Appl. 49(8), 13–17 (2012)

    Google Scholar 

  28. Dongare, Y.V., Dhabe, P.S., Deshmukh, S.V.: RDBNorma: a semi-automated tool for relational database schema normalization up to third normal form. Int. J. Database Manag. Syst. (IJDMS) 3(1), 133–154 (2011)

    Article  Google Scholar 

  29. Demba, M.: Algorithm for relational database normalization up to 3NF. Int. J. Database Manag. Syst. 5(3), 39–51 (2013)

    Article  Google Scholar 

  30. Paul, M., Mather, P.M.: Decision tree based classification of remotely sensed data. In: 22nd Asian Conference on Remote Sensing, pp. 1–20 (2001)

  31. Rosen, K.H.: Discrete Mathematics and Its Applications. Graw Hill, New York (2002)

  32. Qingzhen, X., Li, M.: A new cluster computing technique for social media data analysis. Clust. Comput. (2017) https://doi.org/10.1007/s10586-017-1436-9

    Article  MathSciNet  Google Scholar 

  33. Xu, Q., Wang, Z., Wang, F., Li, J.: Thermal comfort research on human CT data modeling. Multimed. Tools Appl. (2017) https://doi.org/10.1007/s11042-017-4537-9

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, Tamil Nadu, India

    Madhumitha Ramamurthy, Ilango Krishnamurthi & Sudhagar Ilango

  2. Department of MCA, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, Tamil Nadu, India

    Shanthi Palaniappan

Authors
  1. Madhumitha Ramamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilango Krishnamurthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudhagar Ilango
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shanthi Palaniappan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madhumitha Ramamurthy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramamurthy, M., Krishnamurthi, I., Ilango, S. et al. Discrete model based answer script evaluation using decision tree rule classifier. Cluster Comput 22 (Suppl 6), 13499–13510 (2019). https://doi.org/10.1007/s10586-018-1987-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1987-4

Keywords

Search

Navigation