Advertisement

Bag of Errors: Automatic Inference of a Student Model in an Electrical Training System

  • Guillermo Santamaría-BonfilEmail author
  • Yasmín Hernández
  • Miguel Pérez-Ramírez
  • G. Arroyo-Figueroa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10633)

Abstract

An indispensable element of any Intelligent Tutoring Systems is the student model since it enables the system to cope with student’s particular needs. Furthermore, data accumulated by educational systems in bug libraries can be exploited to build a student model by data mining methods. In this work, we built a student model for a virtual reality system used by a Mexican utility to train electricians in operations with medium tension energized lines using its bug libraries. First, errors are mapped to features using a Bag-of-Errors scheme. Additional information about the courses, and the students is also incorporated. Then, a Decision Tree is employed to build the student model. Finally, several student models are built, and compared in terms of Accuracy, Sensitivity, and Specificity. Results show that the proposed model is able to identify trained/untrained students with high accuracy. Moreover, these models shed light on critical task knowledge components which may be used to improve the learning experience of technical operators.

Keywords

Student model Bag of errors Classification and regression trees Variable importance 

Notes

Acknowledgments

GS-B thanks the Consejo Nacional de Ciencia y Tecnología for the support provided under the Cátedra-Conacyt contract 969.

References

  1. 1.
    Nkambou, R., Bourdeau, J., Mizoguchi, R.: Introduction: what are intelligent tutoring systems, and why this book? In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems. SCI, vol. 308, pp. 1–12. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14363-2_1CrossRefzbMATHGoogle Scholar
  2. 2.
    Ranganathan, R., Vanlehn, K., Van de Sande, B.: What do students do when using a step-based tutoring system? Res. Pract. Technol. Enhanc. Learn. 9(2), 323–347 (2014)Google Scholar
  3. 3.
    Woolf, B.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Morgan Kaufmann Publishers, Burlington (2009)Google Scholar
  4. 4.
    Günel, K., Aşliyan, R.: Extracting learning concepts from educational texts in intelligent tutoring systems automatically. Expert Syst. Appl. 37(7), 5017–5022 (2010)CrossRefGoogle Scholar
  5. 5.
    Hernández, Y., Cervantes-Salgado, M., Pérez-Ramírez, M., Mejía-Lavalle, M.: Data-driven construction of a student model using Bayesian networks in an electrical domain. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds.) MICAI 2016, Part II. LNCS (LNAI), vol. 10062, pp. 481–490. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62428-0_39CrossRefGoogle Scholar
  6. 6.
    Vanlehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Ed. 16(3), 227–265 (2006)Google Scholar
  7. 7.
    Vanlehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)CrossRefGoogle Scholar
  8. 8.
    Ayala-García, A., Galván-Bobadilla, I., Arroyo, G., Pérez-Ramírez, M., Muñoz-Román, J.: Virtual reality training system for maintenance and operation of high-voltage overhead power lines. Virtual Real. 20(1), 27–40 (2016)CrossRefGoogle Scholar
  9. 9.
    Sison, R., Shimura, M.: Student modeling and machine learning. Int. J. Artif. Intell. Educ. 9(1), 128–158 (1994)Google Scholar
  10. 10.
    Cao, N., Cui, W.: Introduction to Text Visualization. Atlantis Press, Paris (2016)CrossRefGoogle Scholar
  11. 11.
    Argotte, L., Hernandez, Y., Arroyo-Figueroa, G.: Intelligent e-learning system for training power systems operators. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011. LNCS (LNAI), vol. 6882, pp. 94–103. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23863-5_10CrossRefGoogle Scholar
  12. 12.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)CrossRefGoogle Scholar
  13. 13.
    Romero, C., Ventura, S., Espejo, P.G., Hervás, C.: Data mining algorithms to classify students. In: Educational Data Mining 2008, Proceedings of the 1st International Conference on Educational Data Mining, Montreal, Québec, Canada, 20–21 June 2008, pp. 8–17 (2008). http://www.educationaldatamining.org/EDM2008/uploads/proc/1_Romero_3.pdf
  14. 14.
    Ibrahim, Z., Rusli, D.: Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In: Proceedings of the 21st Annual SAS Malaysia Forum, pp. 1–6 (2007)Google Scholar
  15. 15.
    Guruler, H., Istanbullu, A., Karahasan, M.: A new student performance analysing system using knowledge discovery in higher educational databases. Comput. Educ. 55(1), 247–254 (2010)CrossRefGoogle Scholar
  16. 16.
    Hernández, Y., Pérez, M.: Open student model for blended training in the electrical tests domain. In: Lagunas, O.P., Alcántara, O.H., Figueroa, G.A. (eds.) MICAI 2015. LNCS (LNAI), vol. 9414, pp. 195–207. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27101-9_14CrossRefGoogle Scholar
  17. 17.
    Hernández, Y., Pérez, M.: A B-learning model for training within electrical tests domain. Intell. Learn. Environ. 87, 43–52 (2014)Google Scholar
  18. 18.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2011).  https://doi.org/10.1007/978-0-387-84858-7CrossRefzbMATHGoogle Scholar
  19. 19.
    Piech, C., Sahami, M., Koller, D., Cooper, S., Blikstein, P.: Modeling how students learn to program. In: Proceedings of the 43rd ACM Technical Symposium on Computer Science Education - SIGCSE 2012, pp. 1–6 (2012)Google Scholar
  20. 20.
    Kwartler, T.: Text Mining in Practice with R. Wiley, Chichester (2017)CrossRefGoogle Scholar
  21. 21.
    Loh, W.: Fifty years of classification and regression trees. Int. Stat. Rev. 82(3), 329–348 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Therneau, T., Atkinson, B., Ripley, B.: rpart: Recursive Partitioning and Regression Trees. R package version 4.1-11 (2017)Google Scholar
  23. 23.
    Loh, W.: Classification and regression trees. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 1(1), 14–23 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guillermo Santamaría-Bonfil
    • 1
    • 2
    Email author
  • Yasmín Hernández
    • 1
  • Miguel Pérez-Ramírez
    • 1
  • G. Arroyo-Figueroa
    • 1
  1. 1.Gerencia de Tecnologías de la InformaciónInstituto Nacional de Electricidad y Energías LimpiasCuernavacaMexico
  2. 2.CONACYT-INEELMexico CityMexico

Personalised recommendations