A formal approach to adaptive content delivery

1. A formal approach to adaptive content delivery Dessislava Vassileva, Department of Inf. Technologies, Sofia University “St. Kliment Ohridski”, BULGARIA October, 30-31, 2008, Sofia, Bulgaria

2. 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 October, 30-31, 2008, Sofia, Bulgaria

3. 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 October, 30-31, 2008, Sofia, Bulgaria

4. A conceptual model of AHS October, 30-31, 2008, Sofia, Bulgaria

5. 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 October, 30-31, 2008, Sofia, Bulgaria

6. Domain1 Domain2 Domain3 LO11 LO12 LO13 LO21 LO31 LO32 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 October, 30-31, 2008, Sofia, Bulgaria

7. WP1 P5 P1 P6 P2 CP1 CP2 WP2 P7 P3 P4 WP3 Adaptation model • Adaptation model includes: • Narrative storyboard (graph, CP, pages) • Link annotation, exam thresholds • Storyboard rules - it used for controlling the e-learning process. October, 30-31, 2008, Sofia, Bulgaria

8. 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 October, 30-31, 2008, Sofia, Bulgaria

9. 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 October, 30-31, 2008, Sofia, Bulgaria

10. 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 October, 30-31, 2008, Sofia, Bulgaria

11. 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} October, 30-31, 2008, Sofia, Bulgaria

12. 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) October, 30-31, 2008, Sofia, Bulgaria

13. 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) October, 30-31, 2008, Sofia, Bulgaria

14. 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) October, 30-31, 2008, Sofia, Bulgaria

15. 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) • If the learner knows all subjects, which participate in precondition for given subject, then the learner can start learning it – (3), (4) October, 30-31, 2008, Sofia, Bulgaria

16. 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): October, 30-31, 2008, Sofia, Bulgaria

17. Adaptive rules–updating LM rules • If the learner passes all control point’s tests for particular subject then the learner knows this subject – (7) • If the learner passes particular control point’s test then she/ he knows learning objects contained in the selected control point path – (8) October, 30-31, 2008, Sofia, Bulgaria

18. 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 October, 30-31, 2008, Sofia, Bulgaria

19. Q & A Thank you for your attention Email : ddessy@gmail.com Skype : ddessy October, 30-31, 2008, Sofia, Bulgaria