a service-oriented approach for implementing an adaptation engine for e-learning
DESCRIPTION
A SERVICE-ORIENTED APPROACH FOR IMPLEMENTING AN ADAPTATION ENGINE FOR E-LEARNING. Dessislava Vassileva, Boyan Bontchev Department of Software Engineering, Sofia University “St. Kliment Ohridski”, BULGARIA. Agenda. Introduction to adaptive e-learning systems - PowerPoint PPT PresentationTRANSCRIPT
09 – 10 April eLSE’2009 1
A SERVICE-ORIENTED APPROACH FOR IMPLEMENTING AN ADAPTATION ENGINE FOR E-LEARNING
Dessislava Vassileva, Boyan Bontchev
Department of Software Engineering, Sofia University “St. Kliment Ohridski”, BULGARIA
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Agenda
• Introduction to adaptive e-learning systems
• Our triangular conceptual model of AHS
• Workflow of adaptive courseware delivery
• Adaptive rules
• Service-oriented architecture
• Conclusions and further work
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Introduction to adaptive e-learning systems – types of adaptation
• What is adapted?– Contents– Sequencing– User interface– Delivery channel– What else?
• How it is adapted?– To different technology platforms used– To different learner characters (based on a learner
model)
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• attempt to be different for different students and groups of students
• attempt to be more adaptive by building a model of the goals, preferences, knowledge and performance of each individual student (user/learner model) and using this model throughout the interaction with the student in order to adapt to the needs of that student
Introduction to adaptive e-learning systems – definition
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Introduction to adaptive e-learning systems – techniques
• Adaptive navigation – link hiding, sorting, annotation
• Adaptive presentation - content of a page according to learner’s knowledge, goals, preferences, performance and etc.
• Adaptive content selection – show, sort or hide search result content
• Adaptive problem solution
• Adaptive user interface
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A triangular conceptual model of AHS 1/2
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A triangular conceptual model of AHS 2/2
Conceptual Model
Learner Model
It contains information for the learner profile. Depending on its meaning, it is stored in Goals and Preferences, Learning Style or Knowledge and Performance sub-models.
Goals and Preferences
Learning Style
Knowledge and Performance
Adap-tation Model
It includes description of each course storyboard graph (in Narrative Storyboard sub-model), metadata (such as link annotations, exam thresholds, etc.) of each narrative storyboard graph (in Narrative Metadata sub-model) and selection logic for passing over particular graph (in Storyboard Rules sub-model).
Narrative Metadata
Narrative Storyboard
Storyboard Rules
Domain Model
It is responsible for structuring of learning content. The content is granulized in LOs, which for theirs part are connected among themselves in relevant knowledge domain ontology. LOs and ontology are described by their metadata (in Content Metadata sub-model) respectively according IEEE LOM specification and Ontology Metadata Vocabulary OMV standard
Ontology graph
Learning objects
Content Metadata
The Adaptation Engine communicates with each of the three sub-models at first level in order to generate and delivery to particular learner the most appropriate learning content for her/him.
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A sample course storyboard graph
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General process workflow
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Activity workflow of the adaptation process
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Rules controlling the adaptation processThree main groups:• starting rules - these rules describe learner
knowledge and the initial conditions for starting a new course
• pass-through graph rules – consist of rules for the graph crawling
• rules updating learner model - related to learner knowledge and performance
Example:k(user_performance(k(user_performance(useriuseri, , subjectjsubjectj, control_point, control_pointkk, ,
pass)) pass)) next_cp_path( next_cp_path(useriuseri, , subjectjsubjectj, , control_pointcontrol_pointkk) )
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Service-oriented architecture 1/2
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Service-oriented architecture 2/2
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Conclusions
Future improvement:• Tuning adaptation mechanism• Introducing learner feedback at
control points and using it for adaptation control:– for adaptive navigation – for adaptive content selection
• Artificial intelligence in adaptation algorithm
• Monitoring of user interaction
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Thank you for your attention!
Emails: [email protected], [email protected]