The document discusses the personalization of learning processes through the classification of student preferences and learning styles based on the Felder-Silverman model. It presents a proposed system architecture for identifying student learning profiles using monitoring mechanisms that track interactions in e-learning environments. The system allows teachers to define observation features to analyze student performance and adapt teaching strategies accordingly.

1. campus Sorocaba
Classification of Learning
Profile Based on
Categories of Student
Preferences
Luciana A. M. Zaina, José Fernando Rodrigues Junior, Maria Angélica C A
Cardieri , and Graça Bressan
Available in:
• English: http://dx.doi.org/10.1109/FIE.2008.4720344
• Portuguese: http://www.icmc.usp.br/~junio/PublishedPapers/Zaina-
et_al-IEEELatinAmerica2011.pdf

2. Introduction
 The personalization of a learning process occurs through
the investigation of the student’s characteristicsstudent’s characteristics. Based
on information obtained from his explicit and implicit
knowledge, it is possible to model his needs within the
context in which he interacts.
 Learning styles are cognitive, affective and psychological
traits that determine how a student interacts and reacts in a
learning environment .
 The Felder-Silverman Learning Style Model is describe by
dimensions of Learning and Teaching Styles, creating a
relationship to learning styles and teaching strategies that
could be adopted to support the student learning style

3. Objectives
 The objective of this work is to present the architecturearchitecture
of a systemof a system which makes the classification of a student
within a given learning profile based on profile models
predefined by the teacher.
 The learning profile is identified from information about
the student’s interaction in a given e-learning
environment, which is obtained from a given monitoring
mechanism.
 The profile models are built according to categories of
student preferences based on the proposal of learning
styles put forward by Felder and Silverman.

4. Architecture proposal (I)
 The architecture enables the teacher to specify the
observation features he considers most suitable within the
teaching scope in question.

5. Architecture proposal (II)
 The analysis of data from the tracking of student interaction
will occur when a student completes a teaching module.

6. Dynamic of LearnPES

7. Learning Objects Selection
Process

8. Thank you!
lzaina@ufscar.br