A Survey of Applications that use Graph-based Educational Data Mining
Resumo
A Mineração de Dados Educacionais baseada em grafos pode ser usada para analisar cenários educacionais que podem ser melhor representados por redes complexas. Para manipular essas redes, vários algoritmos foram propostos e implementados em ferramentas que facilitam o desenvolvimento de aplicações práticas. Este artigo apresenta um levantamento de aplicações no domínio educacional que implementaram mineração de dados baseada em grafos, e foram publicados entre 2000 e 2019. Ao total, 30 artigos foram selecionados. As informações extraídas desses trabalhos incluem as questões de pesquisa que eles propuseram responder, a representação gráfica adotada e os algoritmos e ferramentas de mineração de dados usados. Métodos não estatísticos foram utilizados para avaliar e interpretar os achados. Os resultados destacam os domínios e tipos de problemas em que a mineração de dados baseada em grafos está sendo usada e os gráficos/redes usados para representar esses contextos, bem como as abordagens de mineração de dados dominantes adotadas para extrair informações dessas estruturas.
Palavras-chave:
Mineração de Dados Educacionais, Grafos, Aplicações Educacionais
Referências
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Baker, R.S. and Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM—Journal of Educational Data Mining, 1(1): 3–17.
Brown, R., Lynch, C., Wang, Y., Eagle, M., Albert, J., Barnes, T., Baker, R.S., Bergner, Y., and McNamara, D.S. (2015). Communities of performance & communities of preference. In EDM (Workshops).
Buluz, B. and Yilmaz, B. (2017). Graph mining approach for modeling academic success. In Signal Processing and Communications Applications Conference (SIU), 2017 25th, pages 1–4. IEEE.
Catetê, V., Hicks, D., Lynch, C., and Barnes, T. (2014). Snag’em: Graph data mining for a social networking game. In Workshop on Graph-Based Educational Data Mining, volume 6, page 10.
Chakrabarti, D. and Faloutsos, C. (2006). Graph mining: Laws, generators, and algorithms. ACM Computing Surveys (CSUR), 38(1): 2.
Chen, P., Lu, Y., Zheng, V.W., Chen, X., and Yang, B. (2018). Knowedu: A system to construct knowledge graph for education. IEEE Access.
Costa, J. d. J., Bernardini, F., Artigas, D., and Viterbo, J. (2019). Mining direct acyclic graphs to find frequent substructures—an experimental analysis on educational data. Information Sciences, 482: 266–278.
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Journal of educational technology & society, 11(3).
Dekel, R. and Gal, K. (2014). On-line plan recognition in exploratory learning environments. In EDM (Workshops).
Eckles, J.E. and Stradley, E.G. (2012). A social network analysis of student retention using archival data. Social Psychology of Education, 15(2): 165–180.
F Lynch, Collin & Barnes, T. & X.L. & G.N. (2017). Graph-based educational data mining (g-edm2017) proceedings. In Proceedings of the 10th International Conference on Educational Data Mining, pages 1–30.
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Figueiredo, D.R. (2011). Introdução às redes complexas. Atualizações em Informática, pages 303–358.
Fire, M., Katz, G., Elovici, Y., Shapira, B., and Rokach, L. (2012). Predicting student exam’s scores by analyzing social network data. In International Conference on Active Media Technology, pages 584–595. Springer.
Garcia-Saiz, D., Palazuelos, C., and Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. In Educational Data Mining, pages 411–439. Springer.
Gardner, J. and Brooks, C. (2018). Co-enrollment networks and their relationship to grades in undergraduate education. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 295–304. ACM.
Gaševi?, D., Zouaq, A., and Janzen, R. (2013). “choose your classmates, your gpais at stake!” the association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10): 1460–1479.
Gitinabard, N., Xue, L., Lynch, C.F., Heckman, S., and Barnes, T. (2017). A social network analysis on blended courses. arXiv preprint arXiv:1709.10215.
Grawemeyer, B., Wollenschlaeger, A., Gutierrez, S., Holmes, W., Mavrikis, M., and Poulovassilis, A. (2017). Using graph-based modelling to explore changes in students’ affective states during exploratory learning tasks.
Jiang, S., Fitzhugh, S.M., and Warschauer, M. (2014). Social positioning and performance in moocs. In Workshop on Graph-Based Educational Data Mining, volume 14.
Kavitha, D., Rao, B.M., and Babu, V.K. (2011). A survey on assorted approaches to graph data mining. International Journal of Computer Applications, 14(1): 43–46.
Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and software technology, 51(1): 7–15.
Kovanovic, V., Joksimovic, S., Gasevic, D., and Hatala, M. (2014). What is the source of social capital? In Workshop on Graph-Based Educational Data Mining, page 1. Citeseer.
Kumar, R. (2014). Cross-domain performance of automatic tutor modeling algorithms. In EDM (Workshops).
Li, F., Long, X., Du, S., Zhang, J., Liu, Z., Li, M., Li, F., Gui, Z., and Yu, H. (2015). Analyzing campus mobility patterns of college students by using gps trajectory data and graph-based approach. In Geoinformatics, 2015 23rd International Conference on, pages 1–5. IEEE.
Lima Filho, S.P., Cavalcanti, M.C., and Justel, C.M. Modelagem em grafos a partir de bancos de dados relacionais.
London, A. and Németh, T. (2014). Student evaluation by graph-based data mining of administrative systems of education. In Proceedings of the 15th International Conference on Computer Systems and Technologies, pages 363–369. ACM.
London, A., Pelyhe, Á., Holló, C., and Németh, T. (2015). Applying graph-based data mining concepts to the educational sphere. In Proceedings of the 16th International Conference on Computer Systems and Technologies, pages 358–365. ACM.
Loureiro, A.A.F. and Goussevskaia, O. (2015). Grafos. Disponível em: [link]. Acessado em, 20.
Lynch, C.F. (2014). Agg: Augmented graph grammars for complex heterogeneous data. In EDM (Workshops).
Lynch, C.F., Barnes, T., Albert, J., and Eagle, M. (2015). Graph-based educational data mining (g-edm2015). CEUR-WS.
Mostafavi, B. and Barnes, T. (2014). Evaluation of logic proof problem difficulty through student performance data. In EDM (Workshops). Citeseer.
Okamoto, K., Asanuma, H., and Kawamoto, K. (2014). A graph-based data mining method for collaborative learning space in learning commons. In World Automation Congress (WAC), 2014, pages 415–420. IEEE.
Patel, N., Sellman, C., and Lomas, D. (2017). Mining frequent learning pathways from a large educational dataset. arXiv preprint arXiv:1705.11125.
Ramos, D.B. et al. (2016). Uma ferramenta baseada em grafo para identificação e visualização de trilhas de aprendizagem.
Rehman, S.U., Khan, A.U., and Fong, S. (2012). Graph mining: A survey of graph mining techniques. In Digital Information Management (ICDIM), 2012 Seventh International Conference on, pages 88–92. IEEE.
Sheshadri, V., Lynch, C., and Barnes, T. (2014). Invis: An edm tool for graphical rendering and analysis of student interaction data. In EDM (Workshops).
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z. (2008). Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 990–998. ACM.
Wejnert, C. (2010). Social network analysis with respondent-driven sampling data: A study of racial integration on campus. Social Networks, 32(2): 112–124.
Yang, C., Park, H., and Heo, J. (2010). A network analysis of interdisciplinary research relationships: The Korean government’s r&d grant program. Scientometrics, 83(1): 77–92.
Yang, D., Sinha, T., Adamson, D., and Rosé, C.P. (2013). Turn on, tune in, dropout: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS Data-driven education workshop, volume 11, page 14.
Yeung, Y.-Y., Liu, T.C.-Y., and Ng, P.-H. (2005). A social network analysis of research collaboration in physics education. American Journal of Physics, 73(2): 145–150.
Zheng, Y., Liu, R., and Hou, J. (2017). The construction of high educational knowledge graph based on mooc. In Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2017 IEEE 2nd Information, pages 260–263. IEEE.
Baker, R.S. and Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM—Journal of Educational Data Mining, 1(1): 3–17.
Brown, R., Lynch, C., Wang, Y., Eagle, M., Albert, J., Barnes, T., Baker, R.S., Bergner, Y., and McNamara, D.S. (2015). Communities of performance & communities of preference. In EDM (Workshops).
Buluz, B. and Yilmaz, B. (2017). Graph mining approach for modeling academic success. In Signal Processing and Communications Applications Conference (SIU), 2017 25th, pages 1–4. IEEE.
Catetê, V., Hicks, D., Lynch, C., and Barnes, T. (2014). Snag’em: Graph data mining for a social networking game. In Workshop on Graph-Based Educational Data Mining, volume 6, page 10.
Chakrabarti, D. and Faloutsos, C. (2006). Graph mining: Laws, generators, and algorithms. ACM Computing Surveys (CSUR), 38(1): 2.
Chen, P., Lu, Y., Zheng, V.W., Chen, X., and Yang, B. (2018). Knowedu: A system to construct knowledge graph for education. IEEE Access.
Costa, J. d. J., Bernardini, F., Artigas, D., and Viterbo, J. (2019). Mining direct acyclic graphs to find frequent substructures—an experimental analysis on educational data. Information Sciences, 482: 266–278.
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Journal of educational technology & society, 11(3).
Dekel, R. and Gal, K. (2014). On-line plan recognition in exploratory learning environments. In EDM (Workshops).
Eckles, J.E. and Stradley, E.G. (2012). A social network analysis of student retention using archival data. Social Psychology of Education, 15(2): 165–180.
F Lynch, Collin & Barnes, T. & X.L. & G.N. (2017). Graph-based educational data mining (g-edm2017) proceedings. In Proceedings of the 10th International Conference on Educational Data Mining, pages 1–30.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996a). From data mining to knowledge discovery in databases. AI magazine, 17(3): 37.
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996b). Advances in knowledge discovery and data mining.
Figueiredo, D.R. (2011). Introdução às redes complexas. Atualizações em Informática, pages 303–358.
Fire, M., Katz, G., Elovici, Y., Shapira, B., and Rokach, L. (2012). Predicting student exam’s scores by analyzing social network data. In International Conference on Active Media Technology, pages 584–595. Springer.
Garcia-Saiz, D., Palazuelos, C., and Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. In Educational Data Mining, pages 411–439. Springer.
Gardner, J. and Brooks, C. (2018). Co-enrollment networks and their relationship to grades in undergraduate education. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 295–304. ACM.
Gaševi?, D., Zouaq, A., and Janzen, R. (2013). “choose your classmates, your gpais at stake!” the association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10): 1460–1479.
Gitinabard, N., Xue, L., Lynch, C.F., Heckman, S., and Barnes, T. (2017). A social network analysis on blended courses. arXiv preprint arXiv:1709.10215.
Grawemeyer, B., Wollenschlaeger, A., Gutierrez, S., Holmes, W., Mavrikis, M., and Poulovassilis, A. (2017). Using graph-based modelling to explore changes in students’ affective states during exploratory learning tasks.
Jiang, S., Fitzhugh, S.M., and Warschauer, M. (2014). Social positioning and performance in moocs. In Workshop on Graph-Based Educational Data Mining, volume 14.
Kavitha, D., Rao, B.M., and Babu, V.K. (2011). A survey on assorted approaches to graph data mining. International Journal of Computer Applications, 14(1): 43–46.
Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and software technology, 51(1): 7–15.
Kovanovic, V., Joksimovic, S., Gasevic, D., and Hatala, M. (2014). What is the source of social capital? In Workshop on Graph-Based Educational Data Mining, page 1. Citeseer.
Kumar, R. (2014). Cross-domain performance of automatic tutor modeling algorithms. In EDM (Workshops).
Li, F., Long, X., Du, S., Zhang, J., Liu, Z., Li, M., Li, F., Gui, Z., and Yu, H. (2015). Analyzing campus mobility patterns of college students by using gps trajectory data and graph-based approach. In Geoinformatics, 2015 23rd International Conference on, pages 1–5. IEEE.
Lima Filho, S.P., Cavalcanti, M.C., and Justel, C.M. Modelagem em grafos a partir de bancos de dados relacionais.
London, A. and Németh, T. (2014). Student evaluation by graph-based data mining of administrative systems of education. In Proceedings of the 15th International Conference on Computer Systems and Technologies, pages 363–369. ACM.
London, A., Pelyhe, Á., Holló, C., and Németh, T. (2015). Applying graph-based data mining concepts to the educational sphere. In Proceedings of the 16th International Conference on Computer Systems and Technologies, pages 358–365. ACM.
Loureiro, A.A.F. and Goussevskaia, O. (2015). Grafos. Disponível em: [link]. Acessado em, 20.
Lynch, C.F. (2014). Agg: Augmented graph grammars for complex heterogeneous data. In EDM (Workshops).
Lynch, C.F., Barnes, T., Albert, J., and Eagle, M. (2015). Graph-based educational data mining (g-edm2015). CEUR-WS.
Mostafavi, B. and Barnes, T. (2014). Evaluation of logic proof problem difficulty through student performance data. In EDM (Workshops). Citeseer.
Okamoto, K., Asanuma, H., and Kawamoto, K. (2014). A graph-based data mining method for collaborative learning space in learning commons. In World Automation Congress (WAC), 2014, pages 415–420. IEEE.
Patel, N., Sellman, C., and Lomas, D. (2017). Mining frequent learning pathways from a large educational dataset. arXiv preprint arXiv:1705.11125.
Ramos, D.B. et al. (2016). Uma ferramenta baseada em grafo para identificação e visualização de trilhas de aprendizagem.
Rehman, S.U., Khan, A.U., and Fong, S. (2012). Graph mining: A survey of graph mining techniques. In Digital Information Management (ICDIM), 2012 Seventh International Conference on, pages 88–92. IEEE.
Sheshadri, V., Lynch, C., and Barnes, T. (2014). Invis: An edm tool for graphical rendering and analysis of student interaction data. In EDM (Workshops).
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z. (2008). Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 990–998. ACM.
Wejnert, C. (2010). Social network analysis with respondent-driven sampling data: A study of racial integration on campus. Social Networks, 32(2): 112–124.
Yang, C., Park, H., and Heo, J. (2010). A network analysis of interdisciplinary research relationships: The Korean government’s r&d grant program. Scientometrics, 83(1): 77–92.
Yang, D., Sinha, T., Adamson, D., and Rosé, C.P. (2013). Turn on, tune in, dropout: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS Data-driven education workshop, volume 11, page 14.
Yeung, Y.-Y., Liu, T.C.-Y., and Ng, P.-H. (2005). A social network analysis of research collaboration in physics education. American Journal of Physics, 73(2): 145–150.
Zheng, Y., Liu, R., and Hou, J. (2017). The construction of high educational knowledge graph based on mooc. In Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2017 IEEE 2nd Information, pages 260–263. IEEE.
Publicado
11/11/2019
Como Citar
OLIVEIRA, João; ALEXANDRINO, Rodrigo; AMBROSIO, Ana Paula.
A Survey of Applications that use Graph-based Educational Data Mining. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1401-1410.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1401.
