Year: 2012
Type: book-chapter
Abstract: IntroductionAccording to initial design of adaptive e-learning, content of an adaptive course should be suitable for students with different profiles (Brusilovsky, 1996).These profiles may contain information about goals, preferences, knowledge level, learning style, rendering psychological profile, and more.Typically, the learning content is developed for some groups of students that have similar values of one or several parameters of the student's profile.For more groups of students an adaptive course is designed, the more personalized it is.Adaptive Hypermedia Systems (AHS) use models and techniques for adaptive content delivery.They are widely used for technology enhanced learning together with applications for adaptive e-learning, intelligent tutoring, adaptable multimedia delivery and adaptive computer games.AHS are entirely oriented to individual learner profiles.In the context of elearning area, AHS deliver hypertext and hypermedia content which is in line with particular set of parameters of individual learner profile or group of learners (Dagger et al, 2005).Adaptive content delivery must be executed in accordance with a pedagogical strategy.Thus, in order to assure high quality of an adaptive course it has to embody sufficient number of teaching strategies.Such strategies are supposed to be appropriate for different types of students diversified according learning style, level of knowledge, shown performance, preferences and specific goals, learning history or learners needs, etc. (Bontchev, Vassileva, 2009).Strategies are realized by techniques for achieving adaptation such as adaptive navigation, adaptive content selection and link annotations (Conlan, 2003).Another less frequently used method for the realization of adaptive content delivery is by using automatically generated curriculums for each student according to his/her profile or through definition of education storyboards, applying appropriate instruction strategies and methods.Except adaptation techniques, AHS must choose parameters of the student profile which are to be used for controlling adaptation process and to be consistent with the applied teaching strategies.Some researchers emphasize the content adaptation to current learner skills and level of knowledge (Karagiannidis and Sampson, 2002), while others include as well other parameters such as learning history, learners' needs, learning styles of given style family, goals and preferences (Velsen, 2008).The present chapter is focused on courseware adaptation to both learning styles and learner's performance as two very important metrics of the learner model.Within the scope of ADOPTA (ADaptive technOlogy-enhanced Platform for eduTAinment) project, there was www.intechopen.comE-Learning -Engineering, On-Job Training and Interactive Teaching 4 developed a software platform for adaptive content delivery based on a conceptual model supporting adaptivity to learning styles and learners performance, i.e. shown knowledge level (Bontchev and Vassileva, 2011).After presenting an overview of the conceptual model and platform architecture, the chapter discusses approaches for construction of course storyboards adaptive to learning styles and knowledge level of individual learners.It provides a description of a methodology for adaptive course storyboard design and management and, next, shows how this methodology may be used for practical development of an adaptive course in XML technologies.Finally, the chapter considers some practical results collected during pilot experiments with the platform using the adaptive course within a field trial with bachelor students in Software Engineering at Sofia University, Bulgaria.The results concern assessment of efficiency of adaptivity and are summarized from survey conducted after finishing the adaptive course.
Source: InTech eBooks
DOI: 10.5772/29340
Authors: Boyan Bontchev, Dessislava Vassilev
Institution: Sofia University "St. Kliment Ohridski"
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W1547520923