The document discusses domain modeling for personalized learning. It defines a domain model as representing domain knowledge through concepts and their relationships. Domain models serve as the basis for individual student models and for indexing and classifying learning content. They can be used to model student knowledge and decide on appropriate next steps for learning. The document describes different types of domain models, including vector, network, conceptual, and procedural models. It also discusses using ontologies and different aspects in domain modeling and applying domain models to student modeling, content indexing, and personalized guidance.

1. Domain Modeling for
Personalized Learning
Peter Brusilovsky
School of Information Sciences,
University of Pittsburgh

2. What is the Domain Model?

3. • What you are using it for?
• A personalized learning prospect: sequencing, navigation
support, and recommendation research
• Enumeration of domain knowledge
• Serve as a basis for individual student models
• Serve as a way to describe, classify and index learning
content
• Provide connections between state of learner knowledge
and relevant content
• to model the learner after interaction with content
(question, step, example, chapter…)
• to decide what is the next best thing to learn
Why Do We Need Domain Models?

4. • Following Sleeman
– Sleeman, D.H.: UMFE: a user modeling front end system.
International Journal on the Man-Machine Studies 23 (1985) 71-88
• User models can be classified by the nature
and form of information contained in the
model as well as the methods of working
with it
– Brusilovsky, P. and Millán, E. (2007) User models for adaptive hypermedia and
adaptive educational systems. The Adaptive Web: Methods and Strategies of
Web Personalization, Springer-Verlag, pp. 3-53.
Classifying Domain Knowledge Models

5. Three “Sleeman” Layers
• Nature
– what is being modeled
• Structure
– how this information is represented
• Functionality
– how models are used
• Tools
– how we (ITS experts) can work with it

6. Structured Doman Models
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
• AKA Network of
“Things”
• Most of the models
can be represented in
this form
• What is the nature of
each DM element?
• How these elements
are organized?

7. Nature: Kind of Knowledge
• What kind of knowledge DE represents?
• Procedural (interpretable)
– How things work? (simulation)
– How to construct things? (building)
– How to evaluate results? (i.e., constraints)
• Conceptual (representational)
– What do you know?

8. Nature: Granularity of Elements
• What is the granularity of modeling?
• Procedural
– Rules
– Procedures and plans
• Conceptual
– Facts – elementary units, 1000s for a domain (AI experts)
– Concepts – fine grain, 100s for a domain (domain experts)
– Topics – coarse grain, 10s for a domain (teachers)
• Only low level KEs can be considered “cognitive” and
checked with curves

9. Structure
• Vector Models (Enumerative)
• Network models (Structured)
– Clusters
– Hierarchy with single connection type
– Heterarchy or network with multiple
connection types

10. Vector Model of Knowledge
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
No connections, just enumeration

11. Network Model of Knowledge
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Connections represent additional knowledge, help in
modeling and adaptation

12. Classic Bug Model
Rule
A Rule B
Rule
C
n Classic Bug Model is formed by independent
rules (skills) with each having various malrules
(misconceptions)

13. More Advanced Network Procedural
Models
• Pedagogical links (prerequisites)
• Skill Hierarchy
– Procedure -> Steps - > Substeps
– GOMS
• Genetic Model
– Adds genetic relationships that represent the
advancement of skills on different levels of mastery
– Goldstein, I. P. (1979) The Genetic graph: a representation for the evolutionof procedural
knowledge. International Journal on the Man-Machine Studies 11 (1), 51-77.

14. Conceptual Models
• Almost all finer-grain conceptual
models are network models
• Semantic Models on the level of facts
– Buenos Aires is a capital of Argentina
• Classification hierarchies (is-a)
• Decomposition hierarchies (part-of)

15. Decomposition Model in ADAPTS
• Hierarchy of Domain
objects
– System/Subsystem
– Replaceable Unit
– Addressable Unit
• Different levels of
components correspond
to different kinds of
knowledge the user may
have
Aircraft (SH-60)
Sonar
Subsystem 1 Subsystem 2
Subsystem 1.2Subsystem 1.1
Replaceable Unit A Replaceable Unit B
. . .
. . .
Addressable Unit X Addressable Unit Y
. . .
Brusilovsky, P. and Cooper, D. W. (2002) Domain, Task, and User Models for an Adaptive Hypermedia Performance Support
System. In: Y. Gil and D. B. Leake (eds.) Proceedings of 2002 International Conference on Intelligent User Interfaces, San
Francisco, CA, January 13-16, 2002, ACM Press, pp. 23-30.

16. Classification Model: Tree of Life
• Tree of Li

17. Conceptual Modeling with Ontologies
• Modern approach to domain modeling used
ontological frameworks
• Allows to represent multiple types of
connections
• Many standard tools and approaches to use from
Semantic Web (development, extraction…)
• We use ontologies for the last 10 years for all
domain modeling work

18. Ontologies for Domain Modeling
• Created ontologies for C, Java, SQL domains
• Ontology-based content indexing
– Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java
Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science
(AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN,
USA, July 13, 2013, pp. 60-63, also available at https://sites.google.com/site/aiedcs2013/proceedings.
• Ontology mapping for multi-system
personalization
– I.e, Database Exploratorium and Mitrovic SQL Tutor
– Sosnovsky, S., Brusilovsky, P., Yudelson, M., Mitrovic, A., Mathews, M., and Kumar, A. (2009)
Semantic Integration of Adaptive Educational Systems. In: T. Kuflik, S. Berkovsky, F. Carmagnola, D.
Heckmann and A. Krüger (eds.): Advances in Ubiquitous User Modelling. Lecture Notes in Computer
Science, Vol. 5830, pp. 134-158.

19. Ontological Domain Model for Java
• Java Ontology
specifies about 500
classes connected
with 3 types of
relations: subClassOf,
partOf/hasPart, and
related
• About 300 classes are
available for indexing
• A class can play one of
two roles in the problem
index: prerequisite or
outcome

20. [20]
Aspect-based Conceptual Modeling in
ADAPTS
CONCEPT
Reeling Machine
CONCEPT
Sonar Data Computer
CONCEPT
Sonar System
Removal
Instructions
Testing
Instructions
Illustrated
Parts
Breakdown
Principles
of
Operation
Principles
of
Operation
Principles
of
Operation
Removal
Instructions
Removal
Instructions
Testing
Instructions
Testing
Instructions
Illustrated
Parts
Breakdown
Illustrated
Parts
Breakdown

21. [21]
User model: multiple aspects, multiple evidence
Certified
CONCEPT
Reeling Machine
CONCEPT
Sonar Data Computer
CONCEPT
Sonar System
ROLE
Removal
Instructions
ROLE
Testing
Instructions
ROLE
IPB
Reviewed
Hands-on
Simulation
AT2 Smith
AD2 Jones
Preference
Reviewed
Hands-on
+
Certified
Reviewed
Hands-on
Hands-on Reviewed
Reviewed
ROLE
Theory of
Operation

22. Application of Domain Models
• Basis for overlay student models
• Basis for content indexing (i.e., which problem,
example, step, page fragment related to which
KE?
• Taken together, it enables
– Student Modeling an Open Student Modeling
– All kinds of personalized guidance (i.e., when to stop,
what is next…)
– All kinds of adaptive presentation

23. Simple overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
yes
no
no
no
yes
yes

24. Simple overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
yes
no
no
no
yes
yes

25. Weighted overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
10
3
0
2
7
4

26. Topic-based Content Indexing
Example 2
Example M
Example 1
Problem m
Example N
Problem K
Topic 1
Topic 2
Topic N
Problem 1
Problem 2
Problem 10
Each content item is assigned to one topic

27. Concept-based Content Indexing
Example 2
Example M
Example 1
Problem 1
Problem 2
Problem K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Examples
Problems
Concepts
Each content item is indexed with several
concepts
Brusilovsky, P. (2003) Developing Adaptive Educational Hypermedia Systems: From Design Models to Authoring Tools. In: T.
Murray, S. Blessing and S. Ainsworth (eds.): Authoring Tools for Advanced Technology Learning Environments: Toward cost-effective
adaptive, interactive, and intelligent educational software. Kluwer: Dordrecht, pp. 377-409.

28. Personalized Guidance
• When to stop? Typical use of skill models
– Mastery learning
• What to do next? Typical use of concept models
• Which knowledge to learn? Knowledge sequencing
• How to learn it? Content sequencing
• Content sequencing (AI makes decision)
– Questions, problems, examples, readings…
– Proactive or remedial content sequencing
• Adaptive navigation support (Human + AI)
Brusilovsky, P. (2007) Adaptive navigation support. In: P. Brusilovsky, A. Kobsa and W. Neidl (eds.): The Adaptive Web: Methods
and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321, Springer-Verlag, pp. 263-290.

29. QuizGuide: Topic-Based Nav. Support
Sosnovsky, S. and Brusilovsky, P. (2015) Evaluation of Topic-based Adaptation and Student Modeling
in QuizGuide. User Modeling and User-Adapted Interaction 25 (4), In Press.

30. NavEx: Concept-based Navigation Support
Yudelson, M. and Brusilovsky, P. (2005) NavEx: Providing Navigation Support for Adaptive Browsing of
Annotated Code Examples. In: Proceedings of 12th International Conference on Artificial Intelligence in
Education, AI-Ed'2005, Amsterdam, the Netherlands, July 18-22, 2005, IOS Press, pp. 710-717

31. Mastery Grids Sequencing Service
Hosseini, R., Hsiao, I.-H., Guerra, J., and Brusilovsky, P. (2015) What Should I Do Next? Adaptive
Sequencing in the Context of Open Social Student Modeling. In: Proceedings of 10th European Conference on
Technology Enhanced Learning (EC-TEL 2015), Toledo, Spain, pp. In Press.

32. Indexing of Content Fragments
Fragment 1
Fragment 2
Fragment K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Node"Concepts"

33. Adaptive Presentation in ADAPTS

34. Domain Modeling: How?
• Manual domain modeling
– Knowledge Engineering
– Expensive, needs several kinds of experts
– Many authoring support systems (i.e., InterBook)
• Automatic, from text
– Fact extraction
– Rule and casual relationship extraction
– Concept and link extraction (uni- bi- tri- grams)
– Topic modeling (LSA, LDA)
– Remedial content sequencing

35. Indexing: How?
• Manual domain modeling
– Manual indexing by experts
• Powerful, expensive
• Supported by many good authoring systems
– Crowdsourced indexing
– Automatic step indexing (model tracing)
– Automatic content indexing (i.e., Java Parser)
• Automatic, from text or usage data
– Naturally automatic indexing
– Scalable but limited use (i.e., texts, sometimes
questions)