ITS 2008 Tutorial

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Description

Proposal for a full day tutorial at ITS 2008 Constraint-Based Tutoring Systems: From Theory to Authoring 1 1 2 Antonija Mitrovic , Brent Martin , Stellan Ohlsson 1Department of Computer Science and Software Engineering, University of Canterbury Private Bag 4800, Christchurch, New Zealand Phone: [phone] Fax: [phone] [e-mail], [e-mail] 2University of Illinois at Chicago, Department of Psychology Behavioral Science Building, 1007 West Harrison Street, Chicago, IL 60607, USA Phone: [phone] Fax: [phone] Email: [e-mail] BIOGRAPHIES Antonija Mitrovic is Professor in the Department of Computer Science and Engineering at the University of Canterbury in New Zealand. She holds a PhD (1994, Artificial Intelligence in Education) and MSc (1991, Machine Learning) in Computer Science, from the University of Nis, Yugoslavia. She is the leader of the Intelligent Computer Tutoring Group, and has developed a number of constraint-based tutors since 1995, all of which have been thoroughly evaluated in real classrooms and proven to be successful. ICTG has recently completed ASPIRE, an authoring system for developing constraint-based tutors. She has (co)authored more than 130 research papers published in international and national journals and conferences. Dr. Brent Martin is Senior Lecturer at the University of Canterbury and a researcher in the Intelligent Computer Tutoring ...

Informations

• Publié par: Nucheg
• Nombre de lectures: 13
• Langue: English

Extrait

Proposal for a full day tutorial at ITS 2008

Constraint-Based Tutoring Systems: From Theory to Authoring

Antonija Mitrovic

1

, Brent Martin

1

, Stellan Ohlsson

2

1

Department of Computer Science and Software Engineering, University of Canterbury

Private Bag 4800, Christchurch, New Zealand

Phone: [phone]

Fax: [phone]

[e-mail]

,

[e-mail]

2

University of Illinois at Chicago, Department of Psychology

Behavioral Science Building, 1007 West Harrison Street, Chicago, IL 60607, USA

Phone: [phone]

Fax: [phone]

Email:

[e-mail]

BIOGRAPHIES

Antonija Mitrovic

is Professor in the Department of Computer Science and Engineering at the University

of Canterbury in New Zealand. She holds a PhD (1994, Artificial Intelligence in Education) and MSc

(1991, Machine Learning) in Computer Science, from the University of Nis, Yugoslavia. She is the leader

of the Intelligent Computer Tutoring Group, and has developed a number of constraint-based tutors since

1995, all of which have been thoroughly evaluated in real classrooms and proven to be successful. ICTG

has recently completed ASPIRE, an authoring system for developing constraint-based tutors. She has

(co)authored more than 130 research papers published in international and national journals and

conferences.

Dr. Brent Martin

is Senior Lecturer at the University of Canterbury and a researcher in the Intelligent

Computer Tutoring Group. He holds a PhD (Artificial Intelligence in Education) from the University of

Canterbury and MSc (Machine Learning) from the University of Waikato. He performs research in

intelligent educational systems and machine learning. Brent is a member of the Intelligent Computer

Tutoring Group.

Stellan Ohlsson

is Professor of Psychology and Adjunct Professor of Computer Science at the University

of Illinois at Chicago (UIC), Chicago, Illinois, USA, since 1996. He received his Ph.D. in psychology at the

University in Stockholm, Sweden, in 1980. He has since held academic appointments in Sweden, Australia

and the USA. He was Senior Scientist at the Learning Research and Development Center (LRDC) in

Pittsburgh 1990-1995. Dr. Ohlsson is currently completing a book length integration of his research in the

areas of creativity, skill acquisition and conceptual change, to be published by Cambridge University Press

under the title

Deep Learning: How the Mind Overrides Experience

.

PREVIOS TUTORIALS

1.

Ohlsson, S., Mitrovic, A. Constraint-based modelling: an introduction. Ron Sun (ed), Proc. 28

th

Annual Conference of the Cognitive Science Society, Vancouver, pp. 2668, 2006 (half-day tutorial).

2.

Mitrovic, A. Ohlsson, S., Martin, B. and Suraweera, P. Authoring Constraint-based Tutoring Systems.

R. Luckin, K. Koedinger, J. Greer (eds), Proc. AIED 2007, Los Angeles, IOS Press, p725 (full-day

tutorial).

OVERVIEW

This tutorial covers both the theory and practice of Constraint-based Modeling and Constraint-based

Tutoring Systems. The first part of the tutorial introduces constraints and Constraint-Based Modeling

(CBM) as a theoretical foundation for Intelligent Tutoring Systems (ITSs). The second part covers

ASPIRE, our new authoring system for developing constraint-based ITSs, and gives participants the

opportunity to experience developing a small ITS in ASPIRE.

We first introduce constraints as a way of representing domain knowledge. Currently, cognitive models

typically cast declarative knowledge as consisting of propositions – knowledge units that encode assertions

(which can be true or false) that support description, deduction and prediction. We have developed an

alternative model of declarative knowledge that consists of constraints – units of knowledge that are more

prescriptive than descriptive, and that primarily support evaluation and judgment. In this tutorial we first

present a formal representation of constraints and explain its conceptual rationale. We then introduce two

applications of constraint-based modeling. The first is the use of constraints as a basis for a machine

learning algorithm that allows a heuristic search system to detect and correct its own errors. From this point

of view, constraint-based learning is a form of adaptive search. This algorithm was originally developed as

a hypothesis about how people learn from errors. We present the algorithm in some detail and briefly

summarize applications to various problems in the psychology of cognitive skill acquisition.

Next we develop in detail the application of constraint-based modeling to the design and

implementation of Intelligent Tutoring Systems. The constraint-based knowledge representation provides a

novel way to represent the target subject matter knowledge, which has the advantage of directly supporting

one of the main functions of expert knowledge in an ITS: To detect student errors. More importantly, the

constraint-based representation provides a theoretically sound and practical solution to the intractable

problem of student modeling. Finally, the constraint-based representation and the associated learning

algorithm provide detailed implications for how to formulate individual tutoring messages. We present

multiple systems that follow this blueprint, together with empirical evaluation data.

In the second part of the tutorial we present our new authoring system (ASPIRE). The goal of ASPIRE

is to make ITS authoring available to educators who have no technical knowledge of ITS or Computer

Science in general. ASPIRE does this by providing extensive authoring support, such that the author, as far

as possible, is always working at the domain knowledge level, not the programming level. We describe its

architecture and functionality, as well as the authoring procedure it supports. The participants will then

have hands-on opportunities to investigate ASPIRE closer, by using it to build a simple tutor.

OBJECTIVES

We will introduce the participants to a novel knowledge representation in which the units of knowledge are

constraints. The tutorial will touch on the differences between constraints and representations of practical

knowledge (production rules) and the standard propositional representation of declarative knowledge. Most

of the first half of the tutorial will be spent on explaining the properties of this knowledge representation

and describing and demonstrating some of its applications to date, including its use as a machine learning

algorithm and as a simulation model of human learning, but with emphasis on its use in the design of

intelligent tutoring systems.

The tutorial will consist of a mixture of lectures, simple exercises, on-line demonstrations and hands-on

activities. The presenters will take turns to present material and take participants through simple paper-and-

pencil exercises, and to demonstrate features of constraint-based systems and ASPIRE, which are available

via Web access. The presenters will also give hands-on demonstrations of systems that incorporate CBM,

and will coach the participants through a practical exercise where they will get the opportunity to build a

constraint-based system for themselves.

SCHEDULE

PART ONE (morning) – Introduction to CBM and its applications

•

Formal representation of constraints and explanation of their conceptual rationale. Brief contrast to

propositional and rule formats. Brief contrast between description, inference and prediction, on the

one hand, and evaluation and judgment, on the other, as cognitive functions; the proper role of each

in an intelligent system.

•

Description of two applications of constraint-based modeling: 1) a machine learning algorithm that

allows a heuristic search system to detect and correct its own errors, and 2) detailed coverage of the

application of constraint-based modeling to the design and implementation of intelligent tutoring

systems (ITS). Use of constraints for the detection of student errors. Utility of CBM in overcoming

the intractable problem of student modeling. Discussion of ITS design implications. Presentation of

several working ITSs that follow the implied design. Description of an example constraint-based

tutoring shell (WETAS) that generalizes this design.

PART TWO (afternoon) – Practical CBM: the ASPIRE ITS authoring system

•

Introduction to ASPIRE, our new authoring system for developing constraint-based ITSs.

Description of the desired authoring procedure. Detailed description of the ASPIRE’s architecture

and functionality provided by ASPIRE, and how it supports this view of authoring. Presentation of

several examples of ITSs developed in ASPIRE.

•

Practical demonstration of the authoring of a tutor, with each step described in detail.

•

Practical lab: participants try to develop a simple tutoring system using ASPIRE. Participants will be

required to perform the following authoring steps by interacting with the tools provided in ASPIRE:

o

Describe the general features of the domain (author fills in text fields)

o

Develop domain ontology (author develops ontology diagrammatically using ASPIRE’s

ontology drawing tool, and specifies additional information via text fields)

o

Describe the problem and solution structures (author selects ontology components and

adds further information, using a GUI form)

o

Provides examples of problems and their solutions (author fills out GUI forms).

o

Run the constraint generator (author selects appropriate control on form)

o

Modify the generated constraints manually (optional, via language-sensitive text editor)

INTENDED AUDIENCE

The constraint-based knowledge representation has potential to be useful in ITSs, including simulation of

human cognitive processes and the implementation of human-engineered systems for practical use.

Researchers who attend the ITS 2008 conference are precisely the right audience for such a message. They

are both the ones who would be interested, and also the very people who can make maximum use of the

constraint-based approach. So far CBM has been used by a relatively small number of researchers, although

the number of ITSs built using it continues to rise. What we want to accomplish with this tutorial is to

highlight the fact that this is a potentially general tool that might be useful on a wide range of problems.

The participants are assumed to have prior knowledge of symbolic computational systems. Some prior

experience of programming in a symbolic language like Lisp, Prolog or rule-based systems would be

helpful, but advanced programming skill is not needed. The participants primarily need to understand the

general notion of a design of symbolic computational system to exhibit intelligent behavior, and have the

basic concept of such as system as consisting of knowledge structures and processes operating over those

knowledge structures.

LIST OF REQUIREMENTS

We will need PowerPoint presentation capability, with the computer networked so that we can access

software systems over the Web on line during the talk. The participants should have machines with Web

access so that they can use ASPIRE, also should preferably sit at desks or have a writing surface in front of

them, because there will be some paper-and-pencil exercises in turning propositions into constraints. We

have developed tutorial handouts to be provided to the participants.