Cluster techniques and prediction models for a digital media learning environment

The present work applies well-known data mining techniques in a digital learning media environment in order to identify groups of students based on their pro le. We generate identi able clusters where some interesting patterns and rules are observed. We generate a neural network predictive model intended to predict the success of the students in the digital media learning environment. One of the goals of this study is to identify a subset of variables that have the biggest impact in student performance with respect to the learning assessments of the digital media learning environment. Three approaches are used to perform the dimensionality reduction of our dataset. The experiments were conducted with over 69 students of health science courses who used the digital media learning environment.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Fernandez Espinosa, Arturo

Advisor dc:contributor.advisor

• Vargas Martin, Miguel

• Data mining
• Neural networks
• Custer techniques
• Learning
• Dimensionality reduction

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/241

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/241