Abstract
This work proposes a tool for the implementation of an educational data mining model that applies automated machine learning and machine learning interpretability. Starting from the selection between different types of educational problems, the tool: allows semi-automatically building the data set, obtaining an optimized machine learning model using automated machine learning and enabling the explanation of results with machine learning interpretability methods. The proposal allows university institutions to draw conclusions on complex problems, requiring a minimum number of experts in data science and providing a framework for both end users and legal entities to inform themselves about results.
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Novillo-Rangone, G.A., Montejano, G.A., Garis, A.G., Molina, W.R. (2023). A Tool for the Implementation of an Educational Data Mining Model Applied to Universities. In: López-López, P.C., Barredo, D., Torres-Toukoumidis, Á., De-Santis, A., Avilés, Ó. (eds) Communication and Applied Technologies. Smart Innovation, Systems and Technologies, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-19-6347-6_14
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DOI: https://doi.org/10.1007/978-981-19-6347-6_14
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