Main Article Content
This paper presents the results literature review, carried out with the objective of identifying prevalent research goals and challenges in the prediction of student behavior in MOOCs, using Machine Learning. The results allowed recognizingthree goals: 1. Student Classification and 2. Dropout prediction. Regarding the challenges, five items were identified: 1. Incompatibility of AVAs, 2. Complexity of data manipulation, 3. Class Imbalance Problem, 4. Influence of External Factors and 5. Difficulty in manipulating data by untrained personnel.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in IJIER journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
 A. Singh, A. Purohit, “A Survey on Methods for Solving Data Imbalance Problem for Classification,”International Journal of Computer Applications, V. 127, N.15, 2015, pp. 0975 – 8887.
 B. Hong, Z. Wei, Y. Yang, “Discovering Learning Behavior Patterns to Predict Dropout in MOOC,” in 12th International Conference on Computer Science and Education (ICCSE), Houston, TX, USA, 2017, pp. 700–704.
 C. Romero, S. Ventura, S. “Educational data science in massive open online courses,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, V. 7, N. 1, 2016.
 D. F. Onah, J. Sinclair, R. Boyatt, “Dropout rates of massive open online courses: behavioural patterns,” in 14th, EDULEARN, EUA, 2014, pp. 5825-5834.
 D. S. R. Vosgerau, J. P. Romanowski, “Estudos de revisão: implicações conceituais e metodológicas,” Revista Diálogo Educacional. Curitiba, vol. 14, n. 41, 2014, pp. 165-189.
 J. A. Greene, C. A. Oswald, J. Pomerantz, “Predictors of Retention and Achievement in a Massive Open Online Course,” American Educational Research Journal, V. 52, N. 5, 2015, pp. 925–955.
 J. Liang, C. LI, L. Zheng, “Machine Learning Application in MOOCs: Dropout Prediction,” in 11th International Conference on Computer Science & Education (ICCSE 2016), Nagoya University, Japan, 2016, pp. 752–57.
 J. A. Ruipérez-Valiente, p. J. Muñoz-merino, d. Leony, c. D. Kloos, “ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform”, Computers in Human Behavior, Volume 47, 2015, pp. 139-148.
 K. F. Hew, C. Qiao, Y. Tang, “Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs,”International Review of Research in Open and Distributed Learning, V. 19, N. 3, 2018, pp. 69-93.
 L. M. B. Manhaes, S. M. S. Costa, J. Zavaleta, G. Zimbrao, “Previsão de Estudantes com Risco de Evasão o Utilizando Técnicas de Mineração de Dados,” in Proceedings of the 22th, SBIE, Campinas, Brasil, 2011, pp. 1500-1510.
 L. Wang, G. Hu, T. Zhou, “Semantic Analysis of Learners Emotional Tendencies on Online MOOC Education,” Sustainability V. 10, N. 192, 2018.
 N. Periwal, K. Rana, “An Empirical Comparison of Models for DropoutProphecy in MOOCs,” in International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 2017, pp. 906–911.
 R. Gotardo, P. Cereda, J. E. Hruschka, “Predição do Desempenho do Aluno usando Sistemas de Recomendação e Acoplamento de Classificadores,” In Proceedings of the 24th, SBIE, Campinas, Brasil, 2013, pp. 2202-2212.
 R. L. Rodrigues, F. P. A. Medeiros, A. S. Gomes, “Modelo de Regressão Linear aplicado à previsão de desempenho de estudantes em ambiente de aprendizagem,” in 24th, SBIE, Campinas, Brasil, 2013, pP. 607-616.
 R. S. Baker, D. Lindrum, M. J. Lindrum, D. Perkowski, “Analyzing early at-risk factors in higher education e-learning courses”. Students at Risk: Detection and Remediation, 2015.
 S. Halawa, D. Greene, J. Mitchell, “Dropout prediction in moocs using learner activity features,” in Proceedings of the European MOOC Summit (EMOOCs 2014)Lausanne, Switzerland, 2014.
 S. Jiang, A. Williams, K. Schenke, M. Warschauer, D. O'dowd, “Predicting MOOC performance with week 1 behavior,” in 7th International Conference on Educational Data Mining, 2014.
 T. L. Durksen, M. W. Chu, Z. F. Ahmad, A. L. Radil, M. L. Daniels, “Motivation in a MOOC: a probabilistic analysis of online learners basic psychological needs,” Springer, Soc. Psychol Educ., 2016.
 W. Xing, R. Wadholm, E. Petakovic, S. Goggins, “Group learning assessment: developing a theory-informed analytics”. Journal of Educational Technology & Society, V. 18 N. 2, 2015, pp. 110-128,
 W. Xing, Chenx., J. Stein,M.Marcinkowski, “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization,”Elservier, Computers in Human Behavior V. 58, 2016, pp. 119-129.
 X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, A. Mclachlan, B. Liu, P. S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, “Top10 algorithms in data mining. Knowledge and Information Systems,” Springer, Knowl Inf Syst, V. 14, 2008, pp. 1–37.
 Y. Chen, Q. Chen, M. Zhao, S. Boyer, K. Veeramachaneni, H. Qu, “DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction,” in IEEE Conference on Visual Analytics Science and Technology (VAST), Baltimore, MD, USA, 2016, pp. 111–120.