Abstract
The increasing usage of computers in education have produced cumulus of data. Educational Data Mining emerged to take advantage of growing educational data. There has been an extensive application of educational data mining to improve Learning Environments, such as Intelligent Tutoring Systems. The gamification is integrated to educational systems to engage students and produce a better learning. We are developing a gamified tutoring system, and we want to know the relevant characteristics of students to be considered in a gamified learning context. In a first stage, we want to promote motivation, self-efficacy, and self-regulated learning. Therefore, we are analyzing several educational datasets, and we are developing online courses to obtain data and in turn to obtain insights about the relationship between diverse characteristics of students and learning.
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This research was supported by Projects TecNM 10463.21-P, TecNM 10265.21-P, and PRODEP 31535 CENIDET-CA-18.
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Hernández, Y., Martínez, A., Ortiz, J., Estrada, H. (2021). Modeling Self-efficacy and Self-regulated Learning in Gamified Learning Environments Through Educational Data Mining. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_23
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