Tendências de Pesquisas em Mineração de Dados Educacionais em MOOCs: um Mapeamento Sistemático
DOI:
https://doi.org/10.5753/rbie.2020.28.0.491Keywords:
MOOCs, Mineração de Dados Educacionais, Mapeamento SistemáticoAbstract
Massive Open Online Courses (MOOCs) são uma modalidade de curso que utilizam plataformas online e atraem diferentes perfis de estudantes, ofertando oportunidades de qualificação - seja formal ou informal - em um formato bastante dinâmico. Uma característica das plataformas que ofertam tais cursos é a capacidade de armazenar uma grande quantidade de dados, o que possibilitou a exploração destes dados por meio de técnicas de Mineração de Dados Educacionais (MDE). Nesse contexto, foi conduzido um mapeamento sistemático de literatura em cinco bases de dados com o propósito de verificar quais as vertentes de estudos em destaque quanto ao uso de MDE em MOOCs. A busca compreendeu o período de 2015 a 2020, sendo que 158 foram selecionados. Os resultados revelaram que estudos relativos à Análise de Comportamento, Predição (de Desempenho, de Abandono, de Conclusão), Mineração de Texto e Sistemas de Recomendação são os mais frequentes. Além disso, foram identificadas áreas com potencial de exploração, como Social Network Analysis (SNA), Digital Learning Ecosystem (DLE) e Análise de Mind Wandering (MW). Ademais, foram levantados métodos e ferramentas usados nas pesquisas, bem como desafios do uso de MDE na pesquisa sobre MOOC. Conclui-se que a questão do desequilíbrio de classes, provocado pela baixa adesão aos cursos, é um dos maiores desafios.
Descargas
Citas
Aldowaha, H.; Al-Samarraiea, H. & Fauzyb, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49.[DOI: https://doi.org/10.1016/j.tele.2019.01.007].[GS Search]
Al-Shabandar, R.; Hussain, A.; Andy Laws, A.; Keight, R.; Lunn, J. & Radi, N. (2017). Machine learning approaches to predict learning outcomes in Massive open online courses. Proceedings of the International Joint Conference on Neural Networks (IJCNN). Anchorage, AK, USA. [DOI: 10.1109/IJCNN.2017.7965922].[GS Search]
An, T.; Krauss, C. & Merceron, A. (2017). Can Typical Behaviors Identified in MOOCs Be Discovered in Other Courses? Proceedings of the 10th International Conference on Educational Data Mining (EDM). Wuhan, China, p.220-225.[GS Search]
Baker, R. S. J.; Isotani, S. & Carvalho, A. M. J. B. (2011). Mineração de dados educacionais: oportunidades para o Brasil. Revista Brasileira de Informática na Educação. v. 19, n. 2, p. 1-12. [DOI:http://dx.doi.org/10.5753/rbie.2011.19.02.03].[GS Search]
Balint, T. A. (2016). Depth Analysis of Problem-Solving Profiles of Students in Open Online Environments. ProQuest LLC, Ph.D. Dissertation, The George Washington University. p. 1-174.[GS Search]
Brinton, C. G.; Buccapatnam, S.; Zheng, L.; Cao, D.; Lan, A. S. & Felix M. F. (2018) On the Efficiency of Online Social Learning Networks. Journal IEEE/ACM Transactions on Networking. v. 26, n. 5, p. 2076-2089. [DOI: 10.1109 / TNET.2018.2859325].[GS Search]
Brooks, C.; Thompson, C. & Stephanie Teasley. (2015). A time series interaction analysis method for building predictive models of learners using log data. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. p. 126–135.[DOI: https://dl.acm.org/doi/10.1145/2723576.2723581].[GS Search]
Butcher, N. (2014). Technologies in Higher Education: mapping the terrain. [online]. New York: Unesco. Disponível em:[Link para o Artigo]. [Acessado 08 Nov. 2019]. [GS Search]
Cobos, R. & Olmos, L. (2018). A Learning Analytics Tool for Predictive Modeling of Dropout and Certificate Acquisition on MOOCs for Professional Learning. Proceedings of the International Conference on Industrial Engineering and Engineering Management (IEEM). Bangkok, Thailand, p. 1533-1537.[DOI:10.1109 / IEEM.2018.8607541].[GS Search]
Davis, D.; Chen, G.; Hauff, C. & Houben, G. J. (2018). Activating learning at scale: A review of innovations in online learning strategies. Computers & Education, 125, 327-344.[DOI: https://doi.org/10.1016/j.compedu.2018.05.019].[GS Search]
Denyer, D. & Tranfield, D. (2009). Producing a systematic review. In: BUCHANAN, D. A.; BRYMAN, A. (Ed.). The SAGE handbook of organizational research methods. London, SAGE, p. 671-689.[GS Search]
Duru, I.; Dogan, G.; Diri, B. (2016). An overview of studies about students' performance analysis and learning analytics in MOOCs. Proceedings of the International Conference on Big Data (Big Data). Washington, DC, USA. [DOI: 10.1109/BigData.2016.7840786].[GS Search]
Fournier, H. & Kop, R. (2015). MOOC Learning Experience Design: Issues and Challenges. International Journal on E-Learning, 14(3), 289-304.[GS Search]
Galileo, M. M; Roca, M.; Barchino, R.; Hernández, R.; Amado-Salvatierra, H. R. (2019). Applying a Digital Learning Ecosystem to Increase the Effectiveness of a Massive Open Online Course. Proceedings of the IEEE Learning With MOOCS (LWMOOCS). Milwaukee, WI, USA.[DOI:10.1109/LWMOOCS47620.2019.8939636].[GS Search]
Guo, S. X.; Sun, X.; Wang, S. X.; Gao, Y. & Feng J. (2019). Attention-Based Character-Word Hybrid Neural Networks With Semantic and Structural Information for Identifying of Urgent Posts in MOOC Discussion Forums. Journal IEEE Access. v. 7, p. 120522-120532.[DOI:10.1109 / ACCESS.2019.2929211].[GS Search]
He, J.; Bailey, J.; Rubinstein, B. I. P. & Zhang, R. Identifying. (2015). At-Risk Students in Massive Open Online Courses. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. p. 1749–1755.[GS Search]
He, J.; Men, C.; Fang, S.; Du, Z.; Liu, J. & Li, M. (2018). Analysis of MOOC Learning Rhythms. Proceedings of the 20th International Conference on High Performance Computing and Communications. Exeter, United Kingdom, p. 1555-1562.[DOI: 10.1109/HPCC/SmartCity/DSS.2018.00255].[GS Search]
Hutt, S.; Hardey, J.; Bixler, R.; Stewart, A.; Risko, E. & D'Mello, S. K. (2017). Gaze-Based Detection of Mind Wandering during Lecture Viewing. Proceedings of the 10th International Conference on Educational Data Mining (EDM). Wuhan, China, p. 25-28.[GS Search]
Joksimović, S.; Kovanovic, V.; Jovanović, J.; Zouaq, A.; Gasevic, D. & Hatala, M. (2015). What do cMOOC participants talk about in social media?: a topic analysis of discourse in a Cmooc. Proceedings of the LAK '15 International Conference on Learning Analytics And Knowledge. P. 156-165.[DOI: https://doi.org/10.1145/2723576.2723609].[GS Search]
Kashyap, A. & Nayak, A. Different Machine Learning Models to Predict Dropouts in MOOCs. Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI). Bangalore, India, p.80-85.[DOI: 10.1109/ICACCI.2018.8554547].[GS Search]
Labarthe, H.; Bouchet, F.; Bachelet, R. &Yacef, K. (2016). Does a Peer Recommender Foster Students' Engagement in MOOCs? Proceedings of the 9th International Conference on Educational Data Mining (EDM). Raleigh, NC, p. 418-423.[GS Search]
Lan, A. S.; Brinton, C. G.; Yang, T. Y & Chiang, M. (2017). Behavior-Based Latent Variable Model for Learner Engagement. Proceedings of the 10th International Conference on Educational Data Mining (EDM), Wuhan, China, p. 64-71.[GS Search]
Lee, Y. (2018) Using Self-Organizing Map and Clustering to Investigate Problem Solving Patterns in the Massive Open Online Course: An Exploratory Study. Journal of Educational Computing, v. 57, no 2, p. 471-490.[DOI: 10.1177/0735633117753364].[GS Search]
Liñán, L. C. & Pérez, A. A. J. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time Evolution. Journal of Educational Technology in Higher Education. v. 12, n. 3, p. 98-112.[DOI: 10.7238/rusc.v12i3.2515].[GS Search]
Lu, X.; Wang, S.; Huang, J.; Chen, W.; & Yan, Z. (2017). What Decides the Dropout in MOOCs? In: DATABASE Systems for Advanced Applications. Cham: Springer International Publishing. p. 316–327. doi: 10.1007/978-3-319-55705-2_25.[GS Search]
Moissa, B.; Gasparini, I. & Kemczinski, A. (2015). Educational Data Mining versus Learning Analytics: estamos reinventando a roda? Um mapeamento sistemático. Proceedings of the XXVI Simpósio Brasileiro de Informática na Educação (SBIE 2015), Brasil, p. 1167-1176.[DOI: 10.5753/cbie.sbie.2015.116].[GS Search]
Northcutt, C. G.; Ho, A. D. & Chuang, I. L. (2016). Detecting and preventing ""multiple-account"" cheating in massive open online courses. Jounal Computers & Education. v. 100, p. 71-80.[DOI:10.1016/j.compedu.2016.04.008].[GS Search]
Petersen, K.; Feldt, R.; Mujtaba, S. & Mattsson, M. (2008). Systematic Mapping Studies in Software Engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, BCS Learning & Development Ltd., Italy, p. 68–77.[GS Search]
Pigeau, A.; Aubert, O. & Prié, Y. (2019). Success Prediction in MOOCs: A Case Study. Proceedings of the 12th International Conference on Educational Data Mining (EDM). Montreal, Canadá, p. 390-395.[GS Search]
Pursel, B.; Zhang, L.; Jablokow, K.; Choi, G. & Velegol, D. (2016). Understanding MOOC students: motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning. v. 32, n. 3, p. 202–217.[DOI: 10.1111/jcal.12131].[GS Search]
Ramos, A.; Faria, P. M.; Faria, A. (2014). Revisão sistemática de literatura: contributo para a inovação na investigação em Ciências da Educação. Revista Diálogo Educ., Curitiba, v. 14, n. 41, p. 17-36.[DOI:10.7213/dialogo.educ.14.041.DS01].[GS Search]
Romero, C. Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Syst. Appl. v. 1, n. 33, p. 135–146.[DOI:10.1016/j.eswa.2006.04.005].[GS Search]
Romero, C. Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. Jounal IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). v. 40, n. 6, 601-618.[DOI: 10.1109 / TSMCC.2010.2053532].[GS Search]
Romero, C. & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. v. 3, n. 1, p. 12-27.[DOI: 10.1002/widm.1075].[GS Search]
Romero, C. & Ventura, S. (2016). Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. v. 7, n. 1, p. 1-20.[DOI:https://doi.org/10.1002/widm.1187].[GS Search]
Shahiria, A. M; Husaina, W. & Rashida, N. A. (2015). A Review on Predicting Student's Performance Using Data Mining Techniques. Computer Science, 72, 414-422.[DOI: https://doi.org/10.1016/j.procs.2015.12.157].[GS Search]
Sukhija, K.; Jindal, M. & Aggarwal, N. (2015) .The recent state of educational data mining: A survey and future visions. Proceedings of the 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE). Amritsar, India.[DOI:0.1109 / MITE.2015.7375344].[GS Search]
Sunar, A. S.; Abbasi, R. A; Davis, H. C.; White, S. & Aljohani, N. R. (2018). Modelling MOOC learners' social behaviours. Jounal Computers in Human Behavior, p. 1-12.[DOI:https://doi.org/10.1016/j.chb.2018.12.013].[GS Search]
Wagner, R.; Passerino, L.; Silveira, S.; Franciscatto, R. & Lima, J. V. (2016). SolAssist Learning: formação em tecnologias assistivas através de um MOOC e uma biblioteca virtual de soluções assistivas. Revista Brasileira de Informática na Educação. v. 24, no 3, p. 62-74.[DOI:10.5753/rbie.2016.24.3.62].[GS Search]
Waheeda, H.; Hassana, S. U.; Aljohanib, N. R.; Hardmand, J.; Alelyanic, S. & Nawazd, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Journal Computers in Human Behavior, v. 104, p. 106-189.[DOI: https://doi.org/10.1016/j.chb.2019.106189].[GS Search]
Wen, Y.; Tian, Y.; Wen, B.; Zhou, Q.; Cai, G. & Liu, S. (2020). Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs. Journal Tsinghua Science and Technology. v. 25, n. 3, p. 336-347.[DOI: 10.26599 / TST.2019.9010013].[GS Search]
Wulf, J.; Blohm, I.; Leimeister, J. M. & Brenner, W. (2014). Massive open online courses. Business & Information Systems Engineering. v. 6, no. 2, p. 111–114.[DOI:https://doi.org/10.1007/s12599-014-0313-9].[GS Search]
Xing, W.; Chen, X.; Stein, J. & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Jounal Computers in Human Behavior. v. 58, p. 119-129. [DOI: https://doi.org/10.1016/j.chb.2015.12.007].[GS Search]
Zheng, S.; Wisniewski, P; Rosson, M. B. & Carroll, J. M. (2016). Ask the Instructors: Motivations and Challenges of Teaching Massive Open Online Courses. Proceeding CSCW '16. Proceedings of the 19th ACM Conference on ComputerSupported Cooperative Work & Social Computing. p. 206-221.[DOI: 10.1145/2818048.2820082].[GS Search]
Archivos adicionales
Published
Cómo citar
Issue
Section
Licencia
Derechos de autor 2020 Vanessa Faria De Souza, Gabriela Trindade Perry
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
