Abstract
This article aims to introduce experiences in STEAM education, acronym for Science, Technology, Engineering, Art and Mathematics. It has been popularized since it has a positive impact as an extracurricular activity, students develop skills that allow them to improve their academic performance. This paper shows the result of an algorithm developed to analyze, classify and predict applying Computational Intelligence to model the behavior of students performing STEAM activities, to predict the performance of students under this type of education. The main contribution of the paper is the set of metrics and rules that can be used as tools for evaluating these activities. Furthermore, it also evaluates the quality of the academic performance by means of metrics in the underlying pedagogical processes. Part of this work uses a database that contains two tables with data that are the result of two web forms, which contains questions submited to the students and teacher. The database built in a SQL database engine called “Modelo_Automatico_STEAM” contains two tables COD_1A, COD_2B each table contains the different columns that are called variables to generate a data-set, to be used in the prediction and classification model. As part of the scope of this work, there is an introduction to STEAM, description of activities developed in the collaborating institutions, en explanation of the integration and coordination mechanisms, and part of the protocol and results.
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Acknowledgments
The authors thank and acknowledge the contribution of them C.A.R.B. (Colombia), CAETI UAI, Argentine Scientific Society, UCSE DASS, Manuel Dorrego School (EMD), UADER FCyT -CdelU, IEEE GTC Argentina, CI2S Labs and PEGI - LR in Argentina.
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López De Luise, D. et al. (2023). A proposal for Classification and Prediction Algorithms in STEAM Experiences. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_15
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