Mineração de dados educacionais em um mooc brasileiro

Authors

  • Vanessa Faria Souza Universidade Federal do Rio Grande do Sul (UFRGS)

DOI:

https://doi.org/10.30612/eadtde.v8i10.11461

Keywords:

Mineração de Dados Educacionais. MOOCs.

Abstract

No contexto atual da educação a distância, os Learning Management System (LMS) permitem o armazenamento de grande volume de dados sobre as atividades realizadas e para compreender a respeito do padrão de comportamento dos alunos nesse ambiente é preciso que os educadores e gestores repensem as abordagens tradicionais de análise desses dados, sendo essencial a utilização de soluções computacionais apropriadas, como a Mineração de Dados Educacionais (MDE). Este tem como objetivo a aplicação de algoritmos de MDE e análise dos resultados de um MOOC brasileiro com 702 alunos. Como resultados apresenta-se o tipo de atributo que contribuiu de maneira mais significativa para conclusão dos alunos e o padrão de comportamento de grupos de alunos que desistem.

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References

ALLEN, I., & SEAMAN, J. (2015). Online Learning Consortium. Acesso em 10 de 03 de 2016, disponível em Online Report Card – Tracking Online Education in the United States, 2015: http://onlinelearningconsortium.org/read/online-report-card-tracking-onlineeducation-united-states-2015.

ALRAIMI, K., ZO, H., & CIGANEK, A. (2015). Understanding the MOOCs continuance: The role of openness and. Computers & Education, pp. 28-38.

ASIF, R., MERCERON, A., & PATHAN, M. (2014). Predicting student academic performance at degree level: a case study. International Journal of Intelligent Systems and Applications, 7(1), 49-61.

BAKER, R. (2010). Data mining for education. International encyclopedia of education, 7, 112- 118.

BAKER, S. (2014). Educational data mining: An advance for intelligent systems in education. IEEE Intelligent systems, 29(3), pp. 78-82.

BALA, M., & OJHA, D. (2012). Study of applications of data mining techniques in education. International Journal of Research in Science and Technology, 1(4), 1-10.

CALDERS, T., & PECHENIZKIY, M. (2012). Introduction to The Special Section onEducational Data Mining. ACM SIGKDD Explorations Newsletter, 13(2), 3-6.

CAMPAGNI, R., MERLINI, D., SPRUGNOLI, R., & VERRI, M. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508-5521.

CHATTI, M., DYCKHOFF, A., SCHROEDER, U., & THÜS, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), pp. 318- 331.

CLOW, D. (2013). MOOCs and the Funnel of Participation. Proceedings LAK '13, (pp. 186- 189). Leuven, Bélgica.

COFFRIN, C., BARBA, P., CORRIN, L., & KENNEDY, G. (2014). Visuzalizing patterns of student engagement and performance in MOOCs. Proceedings - LAK2014 - Learning Analytics and Knowledge. Indianapolis, USA.

COOPER, S., & SAHAMI, M. (2013). Reflections on Stanford’s MOOCs. New possibilities in online education create new challenges. Communications of the acm, 56(2), 28-30.

CROSSLEY, S., PAQUETTE, L., DASCALU, M., MCNAMARA, D., & BAKER, R. (2016). Combining ClickStream Data with NLP Tools to Better. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge. ACM (pp. 6-14). Edinburgh, U.K.: ACM - Association for Computing Machinery.

DUTT, A., AGHABOZRGI, S., ISMAIL, M., & MAHROEIAN, H. (2015). Clustering Algorithms Applied in Educational Datamining. International Journal of Information and Electronics Engineering, 5(2), 112-116.

ELMASRI, R., & NAVATHE, S. (2011). Sistemas de Banco de Dados (6a. ed.). São Paulo: Pearson Addison Wesley.

FAYYAD, U., PIATETSKY-SHAPIRO, G., & SMYTH, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), pp. 27- 34.

GUO, P., KIM, J., & RUBIN, R. (2014). How video production affects student engagement: An empirical study of mooc videos. Proceedings of the first ACM conference on Learning@ scale conference (pp. 41-50). Atlanta, Georgia, USA: ACM - Association for Computing Machiinery.

HAN, J., PEI, J., & KAMBER, M. (2011). Data mining: concepts and techniques (3. ed.). Waltham, MA: Elsevier. Hew, K., & Cheung, W. (2014). Students and Instructors use of massive open online courses (MOOCs): motivations and challenges. Educacional Research Review, pp. 45-58.

HU, Y., LO, C., & SHIH, S. (2014). Developing early warning systems to predict students’ online learning. Computers in Human Behavior, 36, pp. 469-478.

HYMAN, P. (2012). In the Year of Disruptive Education. Communications of the acm, 55(12), 20-22.

JEEVALATHA, T., ANANTHI, N., & KUMAR, D. (2014). Performance Analysis of Undergraduate Students Placement Selection using Decision Tree Algorithms. International Journal of Computer Applications, 108(15), 27-31.

JORDAN, K. (2015). Massive Open Online Course Completion Rates Revisited: Assessment, Length and Attrition. The International Review of Research in Open and Distributed Learning, 16(3).

KALTURA. (2016). The State of Video in Education 2016: A Kaltura Report. Acesso em 20 de julho de 2019, disponível em Kaltura: https://corp.kaltura.com.

KHALIL, M., & EBNER, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 1-19. Khan, S. (2012). The one world schoolhouse: Education reimagined. New Yourk: Twelve.

MUÑOZ-MERINO, P., RUIPÉREZ-VALIENTE, J., ALARIO-HOYOS, C., PEREZ-SANAGUSTIN, M., & KLOOS, C. (2014). Precise Effectiveness Strategy for Analyzing the Effectiveness of Students. Computer in Human Behavior, pp. 1-11.

NANFITO, M. (2014). MOOCs: Opportunities, impacts, and challenges: massive open online courses in colleges and universities. Createspace - Amazon. Natek, S., & Zwilling, M. (2014). Student data mining solution–knowledge management system related. Expert Systems with Applications, 41(14), 6400-6407.

PARDO, A., & KLOOS, C. (2011). Stepping out of the box: towards analytics outside the learning management system. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 163-167). Banff, Canada: ACM.

RAMAMOHAN, Y., VASANTHARAO, K., CHAKRAVARTI, C., & RATNAM, A. (2012). A study of data mining tools in knowledge discovery process. International Journal of Soft Computing and Engineering (IJSCE), 2(3), 2231-2307. 130

RIGO, S., CAMBRUZZI, W., BARBOSA, J., & CAZELLA, S. (2014). Aplicações de Mineração de Dados Educacionais e Learning Analytics com foco na evasão escolar: oportunidades e desafios. Revista Brasileira de Informática na Educação, 22(1), 132- 146.

ROMERO, C., & VENTURA, S. (2010). Educational Data Mining: A Review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, 40(6), pp. 601-618.

ROMERO, C., & VENTURA, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.

ROMERO, C., ZAFRA, A., LUNA, J., & VENTURA, S. (2013). Association rule mining using genetic programming using genetic programming to provide feedback to instructors from multiple‐choice quiz data. Expert Systems, 30(2), 162-172.

SANDEEN, C. (2013). Integrating MOOCs into Traditional Higher Education: The emerging "MOOC 3.0" Era. The Magazine of Higher Learning, pp. 34-39.

SELVAN, A., BELEYA, P., MUNIANDY, M., HENG, L., & REMENDRAN, C. (2015). Minimizing Student Attrition in Higher Learning Institutions in Malaysia Using Support Vector Machine. Journal of Theoretical and Applied Information Technology, 71(3), 377-385.

SHAHIRI, A., HUSAIN, W., & RASHID, N. (2015). A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 72, pp. 414-422.

SHALEENA, K., & SHAIJU, P. (2015). Data Mining Techniques for Predicting Student Performance. Engineering and Technology (ICETECH) (pp. 1-3). Coimbatore, TN, India: IEEE.

SIEMENS, G.; LONG, P. (2011). Penetrating the Fog: Analytics in Learning and Education. Educase Review, 46(5), pp. 30-40.

WILKOWSKI, J., DEUTSCH, A., & RUSSELL, D. (2014). Student Skill and Goal Achievement in the Mapping with Google MOOC. L@S 2014 - Student Skills and Behavior (pp. 3-10). Atlanta, Georgia, USA.: ACM.

YADAV, S., BHARADWAJ, B., & PAL, S. (2012). Data Mining Applications: A comparative for predicting student's performance. International Journal of Innovative Technology & Creative Engineering, 1(12), pp. 13-19.

YOU, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, pp. 23-30.

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Published

2020-12-11

How to Cite

Souza, V. F. (2020). Mineração de dados educacionais em um mooc brasileiro. EaD & Tecnologias Digitais Na Educação, 8(10), 62–78. https://doi.org/10.30612/eadtde.v8i10.11461

Issue

Vol. 8 No. 10 (2020)

Section

Artigos

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