Abstract
Educational 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
WEKA is open source software issued under the GNU General Public License that contains a collection of ML algorithms [13]. Available at http://www.cs.waikato.ac.nz/ml/weka.
- 2.
Available in the WEKA tool.
- 3.
\(Precision = \frac{VP}{VP + FP}\).
- 4.
\(Recall = \frac{VP}{VP + FN}\).
- 5.
\(F-Measure = \frac{2 \times Recall \times Precision}{Recall + Precision}\).
References
Abdul, A., Vermeulen, J., Wang, D., Lim, B.Y., Kankanhalli, M.: Trends and trajectories for explainable, accountable and intelligible systems: an HCI research agenda. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–18. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3173574.3174156
Ahmed, A., Elaraby, I.S.: Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)
Anuradha, J., Tisha, Ramachandran, V., Arulalan, K.V., Tripathy, B.K.: Diagnosis of ADHD using SVM algorithm. In: Proceedings of the Third Annual ACM Bangalore Conference, COMPUTE 2010, Association for Computing Machinery, New York (2010)
Araújo, A.P.d.Q.C.: Avaliação e manejo de criança com dificuldade escolar e distúrbio de atenção. Jornal de Pediatria 78, S104–S110 (2002)
American Psychological Association et al.: DSM-5: Manual diagnóstico e estatístico de transtornos mentais. Artmed Editora (2014)
Brownlee, J.: How to use ensemble machine learning algorithms in Weka (2016). https://machinelearningmastery.com/use-ensemble-machine-learning-algorithms-weka/. Accessed 08 Apr 2022
Cardoso, L., Mollica, A.M.V., Sales, A.M., Araújo, L.C.: O lúdico e a aprendizagem de crianças com transtorno de déficit de atenção/hiperatividade. Revista Científica FAGOC-Multidisciplinar 3(2) (2019)
Carvalho, M.P.d.: Por que tantos meninos vão mal na escola? critérios de avaliação escolar segundo o sexo. Cadernos de Pesquisa (2007)
Cohen, W.W.: Fast effective rule induction. In: Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)
Cortez, M.T., Pinheiro, Â.M.V.: TDAH e escola: incompatibilidade? Paidéia 13(19) (2018)
Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M., Gagné, C.: Deap: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(1), 2171–2175 (2012)
Frazier, T.W., Youngstrom, E.A., Glutting, J.J., Watkins, M.W.: ADHD and achievement: meta-analysis of the child, adolescent, and adult literatures and a concomitant study with college students. J. Learn. Disabil. 40(1), 49–65 (2007)
Garner, S.: Weka: the Waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference (1995)
Goldberg, D.E.: Genetic algorithms in search. In: Optimization, and Machine Learning (1989)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. Wiley, Hoboken (2004)
He, W.: Examining students’ online interaction in a live video streaming environment using data mining and text mining. Comput. Hum. Behav. 29(1), 90–102 (2013). https://doi.org/10.1016/j.chb.2012.07.020. Including Special Section Youth, Internet, and Wellbeing
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Holland, J.: Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control Artif. Intell. (1975)
Jandre, C., Santos, B.C., Balbino, M., de Miranda, D., Zárate, L.E., Nobre, C.: Analysis of school performance of children and adolescents with attention-deficit/hyperactivity disorder: a dimensionality reduction approach. In: HEALTHINF, pp. 155–165 (2021)
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., Wortman Vaughan, J.: Interpreting interpretability: understanding data scientists’ use of interpretability tools for machine learning. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020)
Kohavi, R.: Wrappers for performance enhancement and oblivious decision graphs. Ph.D. thesis, Stanford University, Department of Computer Science, Stanford University (1995)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 2, pp. 1137–1143. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Koltermann, G.: Sintomas de TDAH, desempenho neurocognitivo e nível socioeconômico em crianças de \(3^\circ \) e \(4^\circ \) anos do ensino fundamental. Mestrado em psicologia, Universidade Federal do Rio Grande do Sul, Porto Alegre (2018)
de Lacerda, E.G., de Carvalho, A.: Introdução aos algoritmos genéticos. Sistemas inteligentes: aplicaçoes a recursos hıdricos e ciências ambientais 1, 99–148 (1999)
Larroca, L.M., Domingos, N.M.: TDAH-investigação dos critérios para diagnóstico do subtipo predominantemente desatento. Psicologia Escolar e Educacional 16(1), 113–123 (2012)
de Lima, C.B., Coelho, C.L.M.: Transtorno de déficit de atenção/hiperatividade-um olhar sob a perspectiva da educação especial inclusiva. Cadernos de Pesquisa em Educação 20(47), 172–192 (2018)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. Curran Associates, Inc. (2017)
Mokhtari, K.E., Higdon, B.P., Başar, A.: Interpreting financial time series with SHAP values. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, CASCON 2019, pp. 166–172. IBM Corporation (2019)
Moreira, S.C., Barreto, M.A.M.: Transtorno de déficit de atenção e hiperatividade: conhecendo para intervir. Revista Práxis 1(2) (2017)
de Pádua Braga, A., de Leon Ferreira, A.C.P., Ludermir, T.B.: Redes neurais artificiais: teoria e aplicações. LTC Editora, Rio de Janeiro (2007)
Pappa, G.L., Freitas, A.A., Kaestner, C.A.: A multiobjective genetic algorithm for attribute selection. In: Proceedings of the 4th International Conference on Recent Advances in Soft Computing (RASC 2002), pp. 116–121. Nottingham Trent University, Nottingham (2002)
Phelan, T.: TDA/TDAH - Transtorno de Déficit de Atenção e Hiperatividade - Sintomas, Diagnósticos e Tratamentos: Crianćas e Adultos. M. Books, São Paulo (2004)
Pongwilairat, K., Louthrenoo, O., Charnsil, C., Witoonchart, C., et al.: Quality of life of children with attention-deficit/hyper activity disorder. J. Med. Assoc. Thai. 88(8), 1062–1066 (2005)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Rahadian, B.A., Dewi, C., Rahayudi, B.: The performance of genetic algorithm learning vector quantization 2 neural network on identification of the types of attention deficit hyperactivity disorder. In: 2017 International Conference on Sustainable Information Engineering and Technology (SIET), pp. 337–341 (2017)
Rangel Júnior, É.d.B., Loos, H.: Escola e desenvolvimento psicossocial segundo percepções de jovens com tdah. Paidéia 21(50), 373–382 (2011)
da Rocha Antony, S.M.: Os ajustamentos criativos da criança em sofrimento: uma compreensão da gestalt-terapia sobre as principais psicopatologias da infância. Estudos e pesquisas em Psicologia 9(2), 356–375 (2009)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
Souza, V.F., dos Santos, T.C.B.: Processo de mineração de dados educacionais aplicado na previsão do desempenho de alunos: Uma comparação entre as técnicas de aprendizagem de máquina e aprendizagem profunda. Revista Brasileira de Informática na Educação 29, 519–546 (2021)
Stein, L.M.: TDE: teste de desempenho escolar: manual para aplicação e interpretação, pp. 1–17. Casa do Psicólogo, São Paulo (1994)
Stiglic, G., Kocbek, S., Pernek, I., Kokol, P.: Comprehensive decision tree models in bioinformatics. PLoS ONE 7(3), e33812 (2012). https://doi.org/10.1371/journal.pone.0033812
Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 1–21 (2020). https://doi.org/10.1109/TNNLS.2020.3027314
UNESCO Institute for Statistics: International standard classification of education: ISCED 2011. UNESCO Institute for Statistics Montreal, Quebec (2012)
Wandera, H., Marivate, V., Sengeh, M.D.: Predicting national school performance for policy making in South Africa. In: 2019 6th International Conference on Soft Computing Machine Intelligence (ISCMI), pp. 23–28 (2019). https://doi.org/10.1109/ISCMI47871.2019.9004323
Acknowledgements
This study was financed in part by the Coordination for the Improvement of Higher Education Personnel - Brasil (CAPES) - Finance Code 001. The authors thank the National Council for Scientific and Technological Development of Brazil (CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico) and the Foundation for Research Support of the Minas Gerais State (FAPEMIG). The work was developed at the Pontifical Catholic University of Minas Gerais, PUC Minas in the Applied Computational Intelligence laboratory - LICAP.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jandre, C., Balbino, M., de Miranda, D., Zárate, L., Nobre, C. (2022). Towards Interpretable Machine Learning Models to Aid the Academic Performance of Children and Adolescents with Attention-Deficit/Hyperactivity Disorder. In: Gehin, C., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2021. Communications in Computer and Information Science, vol 1710. Springer, Cham. https://doi.org/10.1007/978-3-031-20664-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-20664-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20663-4
Online ISBN: 978-3-031-20664-1
eBook Packages: Computer ScienceComputer Science (R0)