Selection and sequencing constraints for personalized courses

Author

Sterbini, Andrea ; Temperini, Marco

Author_Institution

Univ. of Roma La Sapienza, Rome, Italy

fYear

2010

fDate

27-30 Oct. 2010

Abstract

The LECOMPS framework, for personalized and adaptive e-learning, is recalled. Its enhancements, regarding the selection and sequencing optimization algorithms that can be applied, are shown. Such enhancements are discussed, both in terms of their implementation and with respect to their effectiveness: basing on a stated learner´s model (the present state of knowledge, and learning styles of the individual learner), and on a definition of course aims (Target Knowledge), we apply the various selection and sequencing algorithms and compare the results; different courses are produced, corresponding to different personalization requirements, that can possibly occur, also in combination.

Keywords

computer aided instruction; educational courses; learning (artificial intelligence); adaptive e-learning; constraint selection; constraint sequencing; learner model; learning objective; learning style; optimization algorithm; personalized course; Adaptation model; Algorithm design and analysis; Conferences; Context; Electronic learning; Optimization; Sorting; Adaptive e-learning; Learning Objectives; Learning Objects Sequencing Algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers in Education Conference (FIE), 2010 IEEE

Conference_Location

Washington, DC

ISSN

0190-5848

Print_ISBN

978-1-4244-6261-2

Electronic_ISBN

0190-5848

Type

conf

DOI

10.1109/FIE.2010.5673146

Filename

5673146