A formal approach to adaptive
content delivery
Dessislava Vassileva,
Department of Inf. Technologies, Sofia
University “St. Kliment Ohridski”,
BULGARIA
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Agenda
• Introduction to adaptive e-learning
systems
• A triangular model of Adaptive
hypermedia systems (AHS)
• Adaptation engine UML activity diagram
• Formal specification of AHS
• Future works
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Introduction to adaptive e-learning
systems
• Definition
– 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
• Techniques for adaptation:
 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
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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A conceptual model of AHS
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Learner model
• Learner model consist of three sub-models:
 Learning goals and preferences
 Learning style - such as visual, auditory,
kinesthetic and others styles; described in a
declarative manner and determined in the very
beginning of the learning by appropriate tests
 Learner’s knowledge & performance
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Domain model
• Domain model - presented by:
 content itself - granulized in LO according the
SCORM standard
 metadata – Learning Object Metadata (LOM) can be
described according the LMS LOM specification
 Ontology threes
Domain1
LO11
LO12
Domain2
LO13
LO21
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Domain3
LO31
LO32
October, 30-31, 2008, Sofia, Bulgaria
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Adaptation model
• Adaptation model includes:
WP1
 Narrative storyboard
P5
P1
(graph, CP, pages)
 Link annotation, exam thresholds
 Storyboard
P2
rules - it used for
CP1
controlling the
e-learning process.
P3
P4
P6
WP2
CP2
P7
WP3
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Adaptive engine
• Manipulate link annotation
• Show/hide fragments of the pages’ content
• Select of the best storyboard graph’s arc according to
adaptation rules and learner model
• Update learner profile – knowledge, performance
based on learners’ test results
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Adaptation engine UML activity
diagram
• The adaptation engine’s main
activities include:
 Finding the best path
 Delivering appropriate link
annotation and page
content
 Generating learner’s test
 Updating paths’ weight and
learner profile
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Predicate logic - definition
• Predicate logic uses a wholly unambiguous formal
language interpreted by mathematical structures
• Predicate logic is extension of propositional logic with
separate symbols for predicates, subjects, and
quantifiers
• Its formulas contain variables which can be quantified
• Predicate - a verb phrase template that describes a
property of objects, or a relationship among objects
represented by the variables
• Quantification - two common quantifiers are the
existential  and universal  quantifiers
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Learner Model - predicates
• user_learning_style(user_id,learning_style, value) as
learning style={visual, auditory, kinesthetic}
• user_knows_subject(user_id, subject_id)
• user_knows_domain(user_id, domain_id)
• user_knows_learning_object(user_id, lo_id)
• user_performance(user_id, subject_id, control_point_id,
value) as value={pass, fail, notReach}
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Domain Model - predicates
•
•
•
•
•
domain_lo(domain_id, lo_id)
parent_lo(lo_parent_id, lo_child_id)
inheritor_lo(lo_main_id, lo_inheritor_id)
test_question_lo(lo_id, test_question_id)
test_answers(test_question_id, answer_id,
value)
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October, 30-31, 2008, Sofia, Bulgaria
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Adaptation Model - predicates
• lo_4_subject(subject_id, lo_id)
• control_point_4_subject(subject_id,
control_point_id)
• cp_path_4_graph(subject_id, path_id)
• page_4_cp_path(path_id, page_id)
• annotation_cp(learning_style, control_point_id,
value)
• link_pages(current_page_id, next_page_id)
• link_pages_annotation(learning_style, link_id,
annotation)
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Adaptation Engine - predicates
• next_cp_path(user_id, subject_id,
previous_cp_id)
• sub_precondition(subject_new_id,
subject_old_id)
• precondition_subject(subject_new_id)
• user_precondition(user_id, subject_id)
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October, 30-31, 2008, Sofia, Bulgaria
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Adaptive rules - starting rules
• If the user knows all learning objects contained in a domain/subject,
then she/he knows that domain/subject – (1),(2)
(1)  useri  domainj ( lok domain _ lo(domainj , lok )  user _ knows _ learning _ object(useri , lok ))
 user _ knows _ domain(useri , domainj )
(2)  useri  subject j ( lok lo _ 4 _ subject(subject j , lok )  user _ knows _ learning _ object(useri , lok ))
 user _ knows _ subject(useri , subject j )
• If the learner knows all subjects, which participate in precondition for
given subject, then the learner can start learning it – (3), (4)
(3)  subject j  useri (user _ knows _ subject(useri , subject j )  sub _ precondition(subjectk , subject j ))
 user _ precondition(useri , subjectk )
(4)  useri (user _ precondition(useri , subject j ))  next _ cp _ path(useri , subject j , null )
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October, 30-31, 2008, Sofia, Bulgaria
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Adaptive rules – pass-through graph
rules
• If the learner passes or not control point’s test,
she/he continues respectively forward (5) or
backward (6):
(5)  k (user _ performance(useri , subject j , control _ po int k , pass))
 next _ cp _ path(useri , subject j , control _ po int k )
(6)  k (user _ performance(useri , subject j , control _ po int k , fail ))
 next _ cp _ path(useri , subject j , control _ po int k 1 )
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October, 30-31, 2008, Sofia, Bulgaria
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Adaptive rules–updating LM rules
• If the learner passes all control point’s tests for
particular subject then the learner knows this subject –
(7)
(7)  k (user _ performance(useri , subject j , control _ po intk , pass))
 user _ knows _ subject(useri , subject j )
• If the learner passes particular control point’s test then
she/ he knows learning objects contained in the
selected control point path – (8)
(8)  k  i, j, d, l , m (user _ performance(useri , subject j , control _ po int l , pass)
 page _ 4 _ cp _ path(pathm , paged )  lo _ 4 _ page(paged , lok ))
 user _ knows _ learning _ object (useri , lok )
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Future works
• More precise formal model
• Evaluate and compare our formal model with
others similar
• Implement adaptation mechanism from our
formal model
• Artificial intellect in adaptive engine
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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Q&A
Thank you for your attention
Email : [email protected]
Skype : ddessy
TENCompetence Workshop - Stimulating Personal Development and Knowledge Sharing
October, 30-31, 2008, Sofia, Bulgaria
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