domain modeling for personalized learning

Domain Modeling for Personalized Learning

Peter Brusilovsky School of Information Sciences,

University of Pittsburgh

What is the Domain Model?

•  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?

•  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

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

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?

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?

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

Structure

•  Vector Models (Enumerative) • Network models (Structured)

– Clusters – Hierarchy with single connection type – Heterarchy or network with multiple

connection types

Vector Model of Knowledge

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N

No connections, just enumeration

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

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)

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.

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)

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.

Classification Model: Tree of Life

•  Tree of Li

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

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.

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]

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



Illustrated Parts

Breakdown

Illustrated Parts

Breakdown

[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

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

Simple overlay model

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N yes no

no

no yes

yes

Weighted overlay model

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N 10 3

0

2 7

4

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

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.

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.

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.

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

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.

Indexing of Content Fragments

Fragment 1

Fragment 2

Fragment K

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N

Node"Concepts"

Adaptive Presentation in ADAPTS

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

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)