Identifying student behavior in MOOCs using Machine Learning

Goals and challenges

Authors

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

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.

Downloads

Download data is not yet available.

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

Downloads

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.

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 for more visit Copyright & License.

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

Most read articles by the same author(s)