Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments

A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:

2019

Institución:

Universidad Tecnológica de Bolívar

Repositorio:

Repositorio Institucional UTB

Idioma:

eng

OAI Identifier:

oai:repositorio.utb.edu.co:20.500.12585/8754

Acceso en línea:

https://hdl.handle.net/20.500.12585/8754

Palabra clave:

Cluster
Education
KNN
Machine learning
VLE
Cluster analysis
Clustering algorithms
E-learning
Education
Forecasting
Learning systems
Machine components
Machine learning
Motion compensation
Nearest neighbor search
Pattern recognition
Students
Cluster
Fuzzy k-means
K nearest neighbor algorithm
K-nearest neighbors
Three categories
Transition zones
Virtual education
Learning algorithms

Rights

openAccess

License

http://creativecommons.org/licenses/by-nc-nd/4.0/