Mineração de dados educacionais em um mooc brasileiro

EaD & Tecnologias Digitais na Educação

View Publication Info
 
 
Field Value
 
Title Mineração de dados educacionais em um mooc brasileiro
 
Creator Souza, Vanessa Faria
 
Subject Mineração de Dados Educacionais. MOOCs.
 
Description 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.
 
Publisher Editora da UFGD
 
Contributor
 
Date 2020-12-11
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artigo Avaliado pelos Pares
 
Format application/pdf
 
Identifier https://ojs.ufgd.edu.br/index.php/ead/article/view/11461
10.30612/eadtde.v8i10.11461
 
Source EaD & Tecnologias Digitais na Educação; v. 8, n. 10 (2020); 62-78
2318-4051
 
Language por
 
Relation https://ojs.ufgd.edu.br/index.php/ead/article/view/11461/6527
https://ojs.ufgd.edu.br/index.php/ead/article/downloadSuppFile/11461/2435
/*ref*/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.
/*ref*/ALRAIMI, K., ZO, H., & CIGANEK, A. (2015). Understanding the MOOCs continuance: The role of openness and. Computers & Education, pp. 28-38.
/*ref*/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.
/*ref*/BAKER, R. (2010). Data mining for education. International encyclopedia of education, 7, 112- 118.
/*ref*/BAKER, S. (2014). Educational data mining: An advance for intelligent systems in education. IEEE Intelligent systems, 29(3), pp. 78-82.
/*ref*/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.
/*ref*/CALDERS, T., & PECHENIZKIY, M. (2012). Introduction to The Special Section onEducational Data Mining. ACM SIGKDD Explorations Newsletter, 13(2), 3-6.
/*ref*/CAMPAGNI, R., MERLINI, D., SPRUGNOLI, R., & VERRI, M. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508-5521.
/*ref*/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.
/*ref*/CLOW, D. (2013). MOOCs and the Funnel of Participation. Proceedings LAK '13, (pp. 186- 189). Leuven, Bélgica.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/ELMASRI, R., & NAVATHE, S. (2011). Sistemas de Banco de Dados (6a. ed.). São Paulo: Pearson Addison Wesley.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/HU, Y., LO, C., & SHIH, S. (2014). Developing early warning systems to predict students’ online learning. Computers in Human Behavior, 36, pp. 469-478.
/*ref*/HYMAN, P. (2012). In the Year of Disruptive Education. Communications of the acm, 55(12), 20-22.
/*ref*/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.
/*ref*/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).
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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
/*ref*/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.
/*ref*/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.
/*ref*/ROMERO, C., & VENTURA, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
/*ref*/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.
/*ref*/SANDEEN, C. (2013). Integrating MOOCs into Traditional Higher Education: The emerging "MOOC 3.0" Era. The Magazine of Higher Learning, pp. 34-39.
/*ref*/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.
/*ref*/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.
/*ref*/SHALEENA, K., & SHAIJU, P. (2015). Data Mining Techniques for Predicting Student Performance. Engineering and Technology (ICETECH) (pp. 1-3). Coimbatore, TN, India: IEEE.
/*ref*/SIEMENS, G.; LONG, P. (2011). Penetrating the Fog: Analytics in Learning and Education. Educase Review, 46(5), pp. 30-40.
/*ref*/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.
/*ref*/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.
/*ref*/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.
 
Rights Direitos autorais 2020 EaD & Tecnologias Digitais na Educação
https://creativecommons.org/licenses/by-nc-sa/3.0/br/
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library