Questões Epistemológicas em Mineração de Dados Educacionais
Resumo
Resultados cientificamente interessantes vêm sendo apresentados em mineração de dados educacionais (MDE). Entretanto, poucos têm discutido sobre questões filosóficas em relação ao tipo de conhecimento produzido nesta área. Este trabalho tem como propósito apresentar duas questões epistemológicas em MDE: (i) uma questão de natureza ontológica sobre o conteúdo do conhecimento obtido; e (ii) uma questão de natureza deontológica, sobre as pautas e os princípios adotados pelo pesquisador na educação, em detrimento dos resultados de sua própria pesquisa. Ao final, algumas considerações e diretrizes são delineadas como resultado da discussão das questões levantadas.
Palavras-chave:
Mineração de Dados Educacionais, Filosofia, Epistemologia, Ontologia, Deontologia
Referências
Alexander, L. e Moore, M. (2016). Deontological ethics. In Zalta, E. N., editor, The Stanford Encyclopedia of Philosophy (Winter 2016 Edition).
Anjewierden, A., Kolloffel, B., e Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. International Workshop on Applying Data Mining in e-Learning (ADML2007).
Ben-Ari, M. (2001). Constructivism in computer science education. Journal of Computers in Mathematics and Science Teaching, 20(1):45–73.
Boyd, D. e Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5):662–679.
Bramer, M. (2007). Introduction to data mining. In Principles of data mining. Springer.
Burbules, N. C. e Linn, M. C. (1991). Science education and philosophy of science: congruence or contradiction? International Journal of Science Education, 13(3):227–241.
Caliskan, A., Bryson, J. J., e Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6):683–695.
Coelho, O. B. e Silveira, I. (2017). Deep learning applied to learning analytics and educational data mining: A systematic literature review. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 28, page 143.
Ernest, P. (2012). The one and the many. In Constructivism in education, pages 477–504. Routledge.
Fayyad, U., Piatetsky-Shapiro, G., e Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37–37.
Gerson, L. P. (2009). The origin of epistemology. In Ancient epistemology. Cambridge University Press.
Gottardo, E., Kaestner, C., e Noronha, R. V. (2012). Previsao de desempenho de estudantes em cursos ead utilizando mineracao de dados: uma estrategia baseada em series temporais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 23.
Kenski, V. M. (2012). Educação e Tecnologias: O novo ritmo da informação. Papirus, Campinas, SP, 8ª edição.
Laudon, K. e Laudon, J. (2016). Global e-business and collaboration. In Essentials of Management Information Systems. Pearson, 12ª edição.
Lazer, D., Kennedy, R., King, G., e Vespignani, A. (2014). The parable of google flu: traps in big data analysis. Science, 343(6176):1203–1205.
Leonelli, S. (2014). What difference does quantity make? on the epistemology of big data in biology. Big data & society, 1(1):2053951714534395.
Li, K. C., Lam, H. K., e Lam, S. S. (2015). A review of learning analytics in educational research. In International Conference on Technology in Education, pages 173–184. Springer.
Manhães, L. M. B., Da Cruz, S. M. S., Costa, R. J. M., Zavaleta, J., e Zimbrão, G. (2011). Previsao de estudantes com risco de evasao utilizando tecnicas de mineracao de dados. In Brazilian symposium on computers in education (simpósio brasileiro de informática na educação - sbie), volume 1.
Mitra, S., Pal, S. K., e Mitra, P. (2002). Data mining in soft computing framework: a survey. IEEE transactions on neural networks, 13(1):3–14.
Morin, E. (2007). Introdução ao pensamento complexo. Sulina.
Morin, E. (2014). Os sete saberes necessários à educação do futuro. Cortez Editora.
Muller, F. d. M. (2007). A noção deontológica de justificação epistêmica. Princípios: Revista de Filosofia (UFRN), 14(22):21–41.
Nandeshwar, A., Menzies, T., e Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12):14984–14996.
Pal, S. K. e Mitra, P. (2004). Pattern recognition algorithms for data mining. Chapman and Hall/CRC.
Papamitsiou, Z. e Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4):49–64.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4):1432–1462.
Rahwan, I. (2018). Society-in-the-loop: programming the algorithmic social contract. Ethics and Information Technology, 20(1):5–14.
Rescher, N. (2012). Introduction. In Epistemology - An introduction to the theory of knowledge. SUNY Press.
Romero, C. e Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6):601–618.
Romero, C., Ventura, S., Pechenizkiy, M., e Baker, R. S. (2010). Handbook of educational data mining. CRC press.
Santos, F. D., Bercht, M., e Wives, L. (2015). Classificação de alunos desanimados em uma vê a: uma proposta a partir da mineração de dados educacionais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 26, page 1052.
Sergis, S. e Sampson, D. G. (2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. In Learning analytics: Fundaments, applications, and trends, pages 25–63. Springer.
Setzer, V. W. e Silva, F. C. S. (2005). Níveis de abstração. In Bancos de Dados: Aprenda o que são, melhore seu conhecimento, construa os seus. Edgard Blücher.
Sipser, M. (2012). Introduction. In Introduction to the Theory of Computation. Cengage Learning.
Steup, M. (2017). Epistemology. In Zalta, E. N., editor, The Stanford Encyclopedia of Philosophy (Fall 2017 Edition).
Sullare, V., Thakur, R., e Mishra, B. (2016). Analysis of student performance using mining technique: a review. Artificial Intelligent Systems and Machine Learning, 8(3):94–97.
Vahdat, M., Ghio, A., Oneto, L., Anguita, D., Funk, M., e Rauterberg, M. (2015). Advances in learning analytics and educational data mining. Proc. of ESANN 2015, pages 297–306.
Winne, P. H. e Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. JEDM | Journal of Educational Data Mining, 5(1):1–8.
Anjewierden, A., Kolloffel, B., e Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. International Workshop on Applying Data Mining in e-Learning (ADML2007).
Ben-Ari, M. (2001). Constructivism in computer science education. Journal of Computers in Mathematics and Science Teaching, 20(1):45–73.
Boyd, D. e Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5):662–679.
Bramer, M. (2007). Introduction to data mining. In Principles of data mining. Springer.
Burbules, N. C. e Linn, M. C. (1991). Science education and philosophy of science: congruence or contradiction? International Journal of Science Education, 13(3):227–241.
Caliskan, A., Bryson, J. J., e Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6):683–695.
Coelho, O. B. e Silveira, I. (2017). Deep learning applied to learning analytics and educational data mining: A systematic literature review. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 28, page 143.
Ernest, P. (2012). The one and the many. In Constructivism in education, pages 477–504. Routledge.
Fayyad, U., Piatetsky-Shapiro, G., e Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37–37.
Gerson, L. P. (2009). The origin of epistemology. In Ancient epistemology. Cambridge University Press.
Gottardo, E., Kaestner, C., e Noronha, R. V. (2012). Previsao de desempenho de estudantes em cursos ead utilizando mineracao de dados: uma estrategia baseada em series temporais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 23.
Kenski, V. M. (2012). Educação e Tecnologias: O novo ritmo da informação. Papirus, Campinas, SP, 8ª edição.
Laudon, K. e Laudon, J. (2016). Global e-business and collaboration. In Essentials of Management Information Systems. Pearson, 12ª edição.
Lazer, D., Kennedy, R., King, G., e Vespignani, A. (2014). The parable of google flu: traps in big data analysis. Science, 343(6176):1203–1205.
Leonelli, S. (2014). What difference does quantity make? on the epistemology of big data in biology. Big data & society, 1(1):2053951714534395.
Li, K. C., Lam, H. K., e Lam, S. S. (2015). A review of learning analytics in educational research. In International Conference on Technology in Education, pages 173–184. Springer.
Manhães, L. M. B., Da Cruz, S. M. S., Costa, R. J. M., Zavaleta, J., e Zimbrão, G. (2011). Previsao de estudantes com risco de evasao utilizando tecnicas de mineracao de dados. In Brazilian symposium on computers in education (simpósio brasileiro de informática na educação - sbie), volume 1.
Mitra, S., Pal, S. K., e Mitra, P. (2002). Data mining in soft computing framework: a survey. IEEE transactions on neural networks, 13(1):3–14.
Morin, E. (2007). Introdução ao pensamento complexo. Sulina.
Morin, E. (2014). Os sete saberes necessários à educação do futuro. Cortez Editora.
Muller, F. d. M. (2007). A noção deontológica de justificação epistêmica. Princípios: Revista de Filosofia (UFRN), 14(22):21–41.
Nandeshwar, A., Menzies, T., e Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12):14984–14996.
Pal, S. K. e Mitra, P. (2004). Pattern recognition algorithms for data mining. Chapman and Hall/CRC.
Papamitsiou, Z. e Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4):49–64.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4):1432–1462.
Rahwan, I. (2018). Society-in-the-loop: programming the algorithmic social contract. Ethics and Information Technology, 20(1):5–14.
Rescher, N. (2012). Introduction. In Epistemology - An introduction to the theory of knowledge. SUNY Press.
Romero, C. e Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6):601–618.
Romero, C., Ventura, S., Pechenizkiy, M., e Baker, R. S. (2010). Handbook of educational data mining. CRC press.
Santos, F. D., Bercht, M., e Wives, L. (2015). Classificação de alunos desanimados em uma vê a: uma proposta a partir da mineração de dados educacionais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 26, page 1052.
Sergis, S. e Sampson, D. G. (2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. In Learning analytics: Fundaments, applications, and trends, pages 25–63. Springer.
Setzer, V. W. e Silva, F. C. S. (2005). Níveis de abstração. In Bancos de Dados: Aprenda o que são, melhore seu conhecimento, construa os seus. Edgard Blücher.
Sipser, M. (2012). Introduction. In Introduction to the Theory of Computation. Cengage Learning.
Steup, M. (2017). Epistemology. In Zalta, E. N., editor, The Stanford Encyclopedia of Philosophy (Fall 2017 Edition).
Sullare, V., Thakur, R., e Mishra, B. (2016). Analysis of student performance using mining technique: a review. Artificial Intelligent Systems and Machine Learning, 8(3):94–97.
Vahdat, M., Ghio, A., Oneto, L., Anguita, D., Funk, M., e Rauterberg, M. (2015). Advances in learning analytics and educational data mining. Proc. of ESANN 2015, pages 297–306.
Winne, P. H. e Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. JEDM | Journal of Educational Data Mining, 5(1):1–8.
Publicado
11/11/2019
Como Citar
BISPO JR., Esdras L..
Questões Epistemológicas em Mineração de Dados Educacionais. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1541-1550.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1541.
