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.
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GS-B thanks the Consejo Nacional de Ciencia y Tecnología for the support provided under the Cátedra-Conacyt contract 969.
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Santamaría-Bonfil, G., Hernández, Y., Pérez-Ramírez, M., Arroyo-Figueroa, G. (2018). Bag of Errors: Automatic Inference of a Student Model in an Electrical Training System. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_15
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