INFSCI 2480 Adaptive Web Systems User Models for Adaptive Hypermedia and Adaptive Educational Systems
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1 INFSCI 2480 Adaptive Web Systems User Models for Adaptive Hypermedia and Adaptive Educational Systems Peter Brusilovsky School of Information Sciences University of Pittsburgh, USA Adaptive systems Classic loop user modeling - adaptation in adaptive systems
2 Intelligent vs. Adaptive 1. Intelligent but not adaptive (no user model!) 2. Adaptive but not really intelligent 3. Intelligent and adaptive 1 Intelligent ES 3 2 Adaptive ES 3 Dimensions of User Models What is being modeled (nature) How this information is represented (structure How the models are constructed and maintained Brusilovsky, P. and Millan, E.: User models for adaptive hypermedia and adaptive educational systems. In: The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol Springer-Verlag, Berlin Heidelberg New York, 2007"
3 How to Model User Knowledge Scalar model The user knowledge level is modeled as one value Example: MetaDoc, CAT Structural model What kind of knowledge? Declarative, procedural, episodic How it relates to expert knowledge? Overlay model -> Bug mode -> Genetic model Overlay Model of Knowledge Domain model The whole body of domain knowledge is decomposed into set of smaller knowledge units A set of concepts, topics, etc User knowledge model (aka student model) Overlay of the Domain model Student knowledge is measured independently for each knowledge unit
4 More power Vector vs. Network Domain Models Vector - no relationships Precedence (prerequisite) relationship is-a, part-of, analogy Wescourt et al, 1977 Genetic relationships Goldstein, 1979 Vector model Concept 1 Concept 4 Concept N Concept 2 Concept 3 Concept 5
5 Network model Concept 1 Concept 4 Concept N Concept 2 Concept 3 Concept 5 Simple overlay model Concept 1 yes Concept 2 yes yes Concept 3 Concept 4 no Concept 5 Concept N no no
6 Simple overlay model Concept 1 yes Concept 2 yes yes Concept 3 Concept 4 no Concept 5 Concept N no no Weighted overlay model Concept 1 10 Concept Concept 3 Concept 4 3 Concept 5 Concept N 0 2
7 Student Modeling Approaches Ad Hoc (1-100) Heuristic and rule-based (qualitative) Simple statisctical (Bush, Atkinson) Probabilistic and Bayesian (BN, D-S ) Fuzzy Neural networks Combine approaches and layered models How to do Course Sequencing Needs a Domain Model Uses classic or weighted overlay model Needs indexing of learning material with domain model May also need a learning goal (also based on domain model)
8 Indexing teaching material Types of indexing One concept per ULM Indexing of ULMs with concepts How to get the ULMs indexed? Manual indexing (closed corpus) Computer indexing (open corpus) Simple case: one concept per learning activity Concept 4 Concept 1 Concept N Concept 2 Concept 3 Concept 5 Random selection if there are no links -Scholar Links can be used to restrict the order
9 Indexing content with concepts Concepts Examples Example 1 Concept 1 Concept 4 Example 2 Example M Concept N Concept 2 Problems Problem 1 Concept 3 Concept 5 Problem 2 Problem K Simple goal model Learning goal as a set of topics
10 More complicated models Sequence, stack, tree Sequencing with models Given the state of UM and the current goal pick up the best topic or ULM within a subset of relevant ones (defined by links) Special cases with multi-topic indexing and several kinds of ULM Applying explicit pedagogical strategy to sequencing
11 Maintaining Overlay Models Adaptive educational systems use problems, questions, and other evaluation activities to model student knowledge If a page is read, an example is browsed, or a problem is solved, knowledge of all involved concepts increases (example: jwadein) Links could be used to propagate knowledge If problem is not solved, the system needs to allocate blame for involved concepts Links could be helpful to avoid noise Models in SIETTE
12 Models for interactive problemsolving support and diagnosis Domain model Concept model (same as for sequencing) Bug model Constraint model Student model Generalized overlay model (works with bug model and constraint model too) Teaching material - feedback messages for bugs/ constraints Bug models Concept A Concept B Concept C Each concept/skill has a set of associated bugs/misconceptions and sub-optimal skills There are help/hint/remediation messages for bugs
13 Do we need bug models? Lots of works on bug models in the between Bugs has limited applicability Problem solving feedback only. Sequencing does not take bugs into account: whatever misconceptions the student has - effectively we only can re-teach the same material Short-term model: once corrected should disappear, so not necessary to keep Constraint Model: SQL-Tutor Domain model: Set of constraints (procedural, evaluation knowledge); Student model: Bug model
14 Models for example-based problem solving support Need to represent problem-solving cases Episodic learner model Every solution is decomposed on smaller components, but not concepts! Keeping track what components were used and when - not an overlay! ELM-PE and ELM-ART - only systems that use this model ADAPTS - an adaptive IETM
15 What s in adaptive content? Schematics Equipment Photos Equipment Simulations (Training) Video clips (Training) Troubleshooting step plus hypermedia support Block diagrams Theory of operation information, custom-selected Illustrations Troubleshooting Step Engineering Data for a specific technician within a specific work context. ADAPTS dynamically assembles custom-selected content. Domain model example CONCEPT Reeling Machine Principles of Operation Removal Instructions Testing Instructions Illustrated Parts Breakdown CONCEPT Sonar Data Computer Principles of Operation Removal Instructions Testing Instructions Illustrated Parts Breakdown CONCEPT Sonar System Principles of Operation Removal Instructions Testing Instructions Illustrated Parts Breakdown
16 Domain content Principles of Operation SUMMARY DETAILS TUTORIAL Principles of operation General Component Location Sonar System Removal instructions Reeling Machine PART OF Testing instructions Parts List System Description Details Power Distribution User model Characterizes user ability at each element of the domain model Size of model is bounded by domain Weights on different types of elements account for learning styles and preferences Can be time sensitive Constrains the diagnostic strategy
17 User model example CONCEPT Reeling Machine Reviewed Hands-on Reviewed Reviewed CONCEPT Sonar Data Computer Simulation Hands-on Hands-on + Certified Preference Reviewed CONCEPT Sonar System Certified Hands-on Reviewed AT2 Smith AD2 Jones ROLE Theory of Operation ROLE Removal Instructions ROLE Testing Instructions ROLE IPB Adaptive content selection Information is custom-selected for a user Level of detail offered depends upon who the user is (i.e., his level of expertise) Selected at a highly granular level, e.g., for each step within a procedure Performance-oriented training is presented as part of content
18 Interest Modeling User interests are typically modeled by overlay models as well Keyword model of user interests (profile) Keyword overlay Concept model of user interests Concept overlay Domain Models A domain model is required for interest modeling Traditional domain model for interest modeling in educational context A taxonomy of interest areas for non-educational areas Example: Domain model for adaptive News system
19 Overlay Model of Interests For each domain concept or taxon an overlay model stores estimated level of interests Ontological Interest Modeling Interests are deduced from the content of interesting documents Jokela, S., Turpeinen, M., and Sulonen, R. (2000) Ontology Development for Flexible Content, Proceedings of the HICSS-33, IEEE Computer Society, January 4-7, 2000, Maui, Hawaii, USA,
20 Spreading Activation Spreading activation over the model may be used for more reliable modeling and to deal with sparsity A. Sieg, B. Mobasher, R. Burke. Web Search Personalization with Ontological User Profiles. In Proceedings of the ACM Sixteenth Conference on Information and Knowledge Management, CIKM 2007 Overlay model + indexing Indexing nodes with concepts InterBook, ELM-ART, ISIS-Tutor, AHA Indexing anchors with concepts StrathTutor Indexing fragments with concepts MetaDoc, AHA, PT Nodes are concepts InterBook, ELM-ART, ISIS-Tutor
21 Overlay model + indexing Indexing nodes with concepts InterBook, ELM-ART, ISIS-Tutor, AHA Indexing anchors with concepts StrathTutor Indexing fragments with concepts MetaDoc, AHA, PT Nodes are concepts InterBook, ELM-ART, ISIS-Tutor Generalized overlay model Knowledge overlay for set of concepts, network of concepts Goals overlay for set of possible goals, tree of goals Stereotypes overla for set of stereotypes
22 Indexing with generalized model fragments are indexed with goals PUSH nodes are indexed with user s tasks HYNECOSUM: nodes are indexed with stereotypes EPIAIM, Anatom-Tutor, C-Book goals are nodes HYPERFLEX
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