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Identifying student behavior in MOOCs using Machine Learning 109 183

Goals and challenges

Authors

  • Vanessa Faria de Souza Universidade Federal do Rio Grande do Sul, Brazil Author
    • Gabriela Perry Universidade Federal do Rio Grande do Sul, Brazil Author

      DOI:

      https://doi.org/10.31686/ijier.vol7.iss3.1318

      Keywords:

      Machine Learning, MOOC, Student behavior

      Abstract

      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.

      Author Biographies

      • Vanessa Faria de Souza, Universidade Federal do Rio Grande do Sul, Brazil

        Graduate Program of Informatics in Education

      • Gabriela Perry, Universidade Federal do Rio Grande do Sul, Brazil

        Graduate Program of Informatics in Education

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      Published

      2019-03-01

      Issue

      Vol. 7 No. 3 (2019): International Journal for Innovation Education and Research

      Section

      Journal Articles

      License

      Copyright (c) 2019 Vanessa Faria de Souza, Gabriela Perry

      Creative Commons License

      This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

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      How to Cite

      de Souza, V. F., & Perry, G. (2019). Identifying student behavior in MOOCs using Machine Learning: Goals and challenges. International Journal for Innovation Education and Research, 7(3), 30-39. https://doi.org/10.31686/ijier.vol7.iss3.1318