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)


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.


Student model Bag of errors Classification and regression trees Variable importance 



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


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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

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