Return to search

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. / UOIT

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOSHDU.10155/241
Date01 August 2012
CreatorsFernandez Espinosa, Arturo
ContributorsVargas Martin, Miguel
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis

Page generated in 0.0018 seconds