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
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOSHDU.10155/241 |
| Date | 01 August 2012 |
| Creators | Fernandez Espinosa, Arturo |
| Contributors | Vargas Martin, Miguel |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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