Open AccessIdentifying student behavior in MOOCs using Machine Learning
Author(s) -
Vanessa Faria de Souza,
Gabriela Trindade Perry
Publication year - 2019
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol7.iss3.1318
Subject(s) - dropout (neural networks) , open access journal , class (philosophy) , machine learning , computer science , artificial intelligence , mathematics education , psychology , medline , scopus , political science , law
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.