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- W4313342301 abstract "AbstractEducational Data Mining (EDM) integrates numerous auxiliary techniques in capturing, processing, and analyzing school data, with the aim of monitoring and evaluating the process of acquiring knowledge. This assessment and closer monitoring of the student can positively help the learning of students with Attention Deficit Hyperactivity Disorder (ADHD), as they are more likely to have school difficulties, especially in the basic subjects: arithmetic, writing, and reading. Therefore, this work, which is an extension of the article by Jandre et al., seeks to complement the prediction of the results found with the VTJ48 and JRip algorithms that lead to high or low performance of de students whit ADHD in the three basic disciplines, adding the analysis of Random Forest, SVM, and ANN models, in addition to the application of the SHAP method to explain the output of the best model obtained, in case it is not explicitly interpretable. With the results obtained, it can be seen that the best prediction for the arithmetic discipline was performed by Random Forest and SVM (tied); in writing it was the ANN; and in reading it was the VTJ48. In addition, among the features that lead students with ADHD to have a high or low school performance are factors related to parental behavior, student gender, mother’s education level, and family financial situation, among others.KeywordsInterpretabilityMachine learningSHAPAcademic performanceSchool performanceADHDChildrenAdolescents" @default.
- W4313342301 created "2023-01-06" @default.
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- W4313342301 date "2022-01-01" @default.
- W4313342301 modified "2023-10-11" @default.
- W4313342301 title "Towards Interpretable Machine Learning Models to Aid the Academic Performance of Children and Adolescents with Attention-Deficit/Hyperactivity Disorder" @default.
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- W4313342301 doi "https://doi.org/10.1007/978-3-031-20664-1_10" @default.
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