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Difficulty Estimation of Exercises on Tree-Based Search Algorithms Using Neuro-Fuzzy and Neuro-Symbolic Approaches

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Advances in Combining Intelligent Methods

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 116 ))

Abstract

A central topic in the educational process concerns the engagement and involvement of students in educational activities that are tailored and adapted to their knowledge level. In order to provide exercises and learning activities of appropriate difficulty, their difficulty level should be accurately and consistently determined. In this work, we present a neuro-fuzzy and a neuro-symbolic approaches that are used to determine the difficulty level of exercises on tree-based search algorithms and we examine their performance. For the estimation of the difficulty level of the exercises, parameters like the number of the nodes of the tree, the number of children of each node, the maximum depth of the tree and the length of the solution path are taken into account. An extensive evaluation study was conducted on a wide range of exercises for blind and heuristic search algorithms. The performance of the approaches has been examined and compared against that of expert tutors. The results indicate quite promising performance and show that both approaches are reliable, efficient and confirm the quality of their exercise difficulty identification.

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References

  1. Hatzilygeroudis, I., Giannoulis, C., Koutsojannis, C.: Combining expert systems and adaptive hypermedia technologies in a web based educational system. In: Fifth IEEE International Conference on Advanced Learning Technologies, ICALT 2005, pp. 249–253. IEEE (2005)

    Google Scholar 

  2. Alexandrou-Leonidou, V., Philippou, G.N.: Teachers’ beliefs About Students Development of the Pre-algebraic Concept of Equation. International Group for the Psychology of Mathematics Education, p. 41 (2005)

    Google Scholar 

  3. Barla, M., Bieliková, M., Ezzeddinne, A.B., Kramár, T., Šimko, M., Vozár, O.: On the impact of adaptive test question selection for learning efficiency. Comput. Educ. 55(2), 846–857 (2010)

    Article  Google Scholar 

  4. Chen, L.H.: Enhancement of student learning performance using personalized diagnosis and remedial learning system. Comput. Educ. 56(1), 289–299 (2011)

    Article  Google Scholar 

  5. Nguyen, M.L., Hui, S.C., Fong, A.C.M.: Large-scale multiobjective static test generation for web-based testing with integer programming. IEEE Trans. Learn. Technol. 6(1), 46–59 (2013)

    Article  Google Scholar 

  6. Verdú, E., Verdú, M.J., Regueras, L.M., de Castro, J.P., García, R.: A genetic fuzzy expert system for automatic question classification in a competitive learning environment. Expert Syst. Appl. 39(8), 7471–7478 (2012)

    Article  Google Scholar 

  7. Conejo, R., Guzmán, E., Perez-De-La-Cruz, J.L., Barros, B.: An empirical study on the quantitative notion of task difficulty. Expert Syst. Appl. 41(2), 594–606 (2014)

    Article  Google Scholar 

  8. Grivokostopoulou, F., Hatzilygeroudis, I.: Teaching AΙ search algorithms. In: A Web-Based Educational System, Proceedings of the IADIS International Conference e-Learning 2013, 23–26 July, pp. 83–90. Prague, Czech Republic (2013)

    Google Scholar 

  9. Grivokostopoulou, F., Perikos, I., Hatzilygeroudis, I.: An educational system for learning search algorithms and automatically assessing student performance. Int. J. Artif. Intell. Educ. (2016). doi:10.1007/s40593-016-0116-x

    Google Scholar 

  10. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. Syst. Man Cybern. IEEE Trans. 23(3), 665–685 (1993)

    Article  Google Scholar 

  11. Hatzilygeroudis, I., Prentzas, J.: Constructing modular hybrid knowledge bases for expert systems. Int. J. Artif. Intell. Tools 10, 87–105 (2001)

    Article  Google Scholar 

  12. Grivokostopoulou, F., Perikos, I., & Hatzilygeroudis, I. (2015). Estimating the Difficulty of Exercises on Search Algorithms Using a Neuro-fuzzy Approach. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on (pp. 866–872). IEEE

    Google Scholar 

  13. Van de Watering, G., Van der Rijt, J.: Teachers’ and students’ perceptions of assessments: a review and a study into the ability and accuracy of estimating the difficulty levels of assessment items. Educ. Res. Rev. 1(2), 133–147 (2006)

    Article  Google Scholar 

  14. Lee, F.L.: Electronic homework: an intelligent tutoring system in mathematics. Doctoral Dissertation, The Chinese University of Hong Kong Graduate School—Division of Education, November (1996). http://www.fed.cuhk.edu.hk/en/cuphd/96fllee/content.htm. Assessed June 2014

  15. Impara, J.C., Plake, B.S.: Teachers’ ability to estimate item difficulty: a test of the assumptions in the Angoff standard setting method. J. Educ. Meas. 35(1), 69–81 (1998)

    Article  Google Scholar 

  16. Leung, E.W.C., Li, Q.: An experimental study of a personalized learning environment through open-source software tools. IEEE Trans. Educ. 50(4), 331–337 (2007)

    Article  Google Scholar 

  17. Hatzilygeroudis, I., Prentzas, J.: Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities. Expert Syst. Appl. 26(4), 477–492 (2004)

    Article  Google Scholar 

  18. Koutsojannis, C., Beligiannis, G., Hatzilygeroudis, I., Papavlasopoulos, C.: Using a hybrid AI approach for exercise difficulty level adaptation. Int. J. Continuing Eng. Educ. Life Long Learn. 17(4), 256–272 (2007)

    Article  Google Scholar 

  19. Perikos, I., Grivokostopoulou, F., Hatzilygeroudis, I., Kovas, K.: Difficulty estimator for converting natural language into first order logic. In: Intelligent Decision Technologies, pp. 135–144. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  20. Grivokostopoulou, F., Hatzilygeroudis, I., Perikos, I.: Teaching assistance and automatic difficulty estimation in converting first order logic to clause form. Artif. Intell. Rev. 1–21 (2013)

    Google Scholar 

  21. Chen, C.M., Duh, L.J.: Personalized web-based tutoring system based on fuzzy item response theory. Expert Syst. Appl. 34(4), 2298–2315 (2008)

    Article  Google Scholar 

  22. Cheng, I., Shen, R., Basu, A.: An algorithm for automatic difficulty level estimation ofmultimedia mathematical test items. Proceedings of the 8th IEEE International Conference on Advanced Learning Technologies, pp. 175–179. IEEE Computer Society, Los Alamitos, CA (2008)

    Google Scholar 

  23. Wauters, K., Desmet, P., Van Den Noortgate, W.: Acquiring item difficulty estimates: a collaborative effort of data and judgment. In: Education Data Mining (2011)

    Google Scholar 

  24. Hatzilygeroudis, I., Prentzas, J.: Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems. Expert Syst. Appl. 27(1), 63–75 (2004)

    Article  MATH  Google Scholar 

  25. Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)

    Article  Google Scholar 

  26. Sevarac, Z., Devedzic, V., Jovanovic, J.: Adaptive neuro-fuzzy pedagogical recommender. Expert Syst. Appl. 39(10), 9797–9806 (2012)

    Article  Google Scholar 

  27. Taylan, O., Karagözoğlu, B.: An adaptive neuro-fuzzy model for prediction of student’s academic performance. Comput. Ind. Eng. 57(3), 732–741 (2009)

    Article  Google Scholar 

  28. Stathacopoulou, R., Grigoriadou, M., Magoulas, G.D., Mitropoulos, D.: A neuro-fuzzy approach in student modeling. In: User Modeling 2003, pp. 337–341. Springer, Berlin, Heidelberg (2003)

    Google Scholar 

  29. Norazah, Y.: Student learning assessment model using hybrid method. Universiti Keba, PhD Thesis (2005)

    Google Scholar 

  30. Iraji, M.S., Aboutalebi, M., Seyedaghaee, N.R., Tosinia, A.: Students classification with adaptive neuro fuzzy. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 4(7), 42 (2012)

    Article  Google Scholar 

  31. Do, Q.H., Chen, J.F.: A neuro-fuzzy approach in the classification of students’ academic performance. Comput. Intell. Neurosci. 2013, 6 (2013)

    Article  Google Scholar 

  32. Kakar, M., Nyström, H., Aarup, L.R., Nøttrup, T.J., Olsen, D.R.: Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys. Med. Biol. 50(19), 4721 (2005)

    Article  Google Scholar 

  33. Hatzilygeroudis, I., Prentzas, J.: Integrated rule-based learning and inference. IEEE Trans. Knowl. Data Eng. 22, 1549–1562 (2010)

    Article  Google Scholar 

  34. Hatzilygeroudis, I., Prentzas, J.: Symbolic-neural rule based reasoning and explanation. Expert Syst. Appl. 42, 4595–4609 (2015)

    Article  Google Scholar 

  35. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)

    Article  Google Scholar 

  36. Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360–363 (2005)

    Google Scholar 

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Acknowledgment

This work was partially supported by the Research Committee of the University of Patras, Greece, Program “Karatheodoris”, project No C901.

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Authors and Affiliations

  1. Department of Computer Engineering and Informatics, University of Patras, 26504, Patras, Greece

    Foteini Grivokostopoulou, Isidoros Perikos & Ioannis Hatzilygeroudis

Authors
  1. Foteini Grivokostopoulou
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  2. Isidoros Perikos
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  3. Ioannis Hatzilygeroudis
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Corresponding author

Correspondence to Foteini Grivokostopoulou .

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Editors and Affiliations

  1. Sch of Engg, Dept of Comp Engg & Informa, University of Patras Sch of Engg, Dept of Comp Engg & Informa, Patras, Greece

    Ioannis Hatzilygeroudis

  2. Fac of Engg & Compu, dept of Compu, Coventry University, Reader in Perv, Fac of Engg & Compu, dept of Compu, Coventry, United Kingdom

    Vasile Palade

  3. Dept of Edu Sci Early child, Lab of info, Democritus Univer Thrace,Sch of Edu Sci Dept of Edu Sci Early child, Lab of info, Alexandroupolis, Greece

    Jim Prentzas

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Grivokostopoulou, F., Perikos, I., Hatzilygeroudis, I. (2017). Difficulty Estimation of Exercises on Tree-Based Search Algorithms Using Neuro-Fuzzy and Neuro-Symbolic Approaches. In: Hatzilygeroudis, I., Palade, V., Prentzas, J. (eds) Advances in Combining Intelligent Methods. Intelligent Systems Reference Library, vol 116 . Springer, Cham. https://doi.org/10.1007/978-3-319-46200-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-46200-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46199-1

  • Online ISBN: 978-3-319-46200-4

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