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Classification of learning profile based on categories of student preferences
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Transcript of Classification of learning profile based on categories of student preferences
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campus Sorocaba
Classification of Learning Profile Based onCategories 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
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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
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Objectives The objective of this work is to present the architecture of a architecture of a
system 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.
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Architecture proposal (I) The architecture enables the teacher to specify the
observation features he considers most suitable within the teaching scope in question.
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Architecture proposal (II) The analysis of data from the tracking of student interaction
will occur when a student completes a teaching module.
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Dynamic of LearnPES
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Learning Objects Selection Process