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Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE Research Institute Emeritus and Adjunct Professor, University of Pennsylvania Visiting Research Professor, College of Information Science and Technology, Drexel University www. scandura.com
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Page 1: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Introduction to AuthorIT

April 10, 2006

Symposium on Knowledge Representation

TICL SIG

Joseph M. Scandura, Ph.D.Chairman, Board Scientific Advisors, MERGE Research Institute

Emeritus and Adjunct Professor, University of Pennsylvania

Visiting Research Professor, College of Information Science and Technology, Drexel University

www. scandura.com

Page 2: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

V. Structural Learning TheoryUsing AuthorIT to Create Instructional Systems

I-A. Content Knowledge Representation

tasks/problems lower & higher order SLT rules

TutorITI-A. Content knowledge w/

III. UCM, capacity/speedIV. Full diagnostic & tutorial

expertise;fully configurable

LearnerIII. U Control Mechanism,

capacity/speedIV. Individual knowledge

V. Structural Analysis viaAuthorIT AutoBuilder Blackboard Editor TutorIT Options I-B. Blackboard Interface

TutorIT displays & Learner responses

copyright scandura 2001-5-56

Page 3: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Problem Structure (AST) Problem Layout Node Attributes

Using Blackboard Editor to Represent Problems & Interfaces as ASTs

Page 4: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Author IT: Structural Knowledge Input-Output Data Structure AST defining Column Subtraction

Page 5: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Author IT: Procedural Knowledge Procedure AST Generating Specified Input and Output Behavior

Page 6: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Author IT

Defining TutorIT Delivery inOptionsTools

Page 7: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

TutorIT: Procedural Knowledge Learner Model and Sample Screen

Page 8: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Tutor IT: Declarative Knowledge (Simple Factual & Structural Knowledge)

State Capitals Building Blocks

Page 9: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

I. What Does AuthorIT Mean for Developers/Researchers?

Faster More Cost-Effective CBI Development Content – use AutoBuilder to define what is to be learned Interface – use Blackboard Editor to define user interface

in easy to use GUI Delivery – use Option Tool (dialog box) to specify one or

more ways for content to be delivered Delivery – TutorIT delivers content to learner in any of the

specified ways (e.g., to conduct definitive research)

Integrated Development Platform Means Quality Control / Uniform Quality

Page 10: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

II. What Does AuthorIT Mean for Developers/Researchers?

Greater Precision To be acquired knowledge can be

represented at whatever level of precision may be desired

Analysis is cumulative – continued as resources allow

Diagnostic precision & tutor effectiveness – increased as resources allow

Utopian goal of guaranteed learning becomes attainable

Page 11: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

III. What Does AuthorIT Mean for Developers/Researchers?

Single Development Cycle –

Multiple Products

Instruction – delivers instruction systematically in optimal order

Diagnostic – quickly determines what learner knows Adaptive – automatically adjusts testing & instruction in

optimal fashion Simulation/Performance Aid – Guides learner step by

step in performing tasks Practice – provides practice on important material

copyright scandura 2001-5-56

Page 12: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Full Range of AuthorIT TutorialsLearning How to Design an Interface

Page 13: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

TutorIT Customizer & Customizer plusAllows Teachers to Easily Customize TutorIT Systems

Easily Customize Existing Tutorials Change/Add Text and/or Teacher’s Voice/Audio for

Instruction Questions Positive Feedback Corrective Feedback

Customized Content Automatically Works with TutorIT

Plus Version Supports All Customizer features Custom files (e.g., Flash) Selecting Author Prescribed TutorIT Options for Students Exporting to Other Networked Computers

Page 14: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Tutor IT Customizer plus Dialog

Page 15: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

What Do TutorIT Customizers Mean for Developers/Teacher/Trainers?

Easily Customize for Different Learners Add or customize text Add or customize audio

with Teacher’s Voice Spanish, Chinese, etc. instead of English

Add or customize Flash demonstrations

Multiple Versions at No Extra Cost Different versions of Products without programming

e.g., for languages, simply re-record & save

Page 16: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Define Input & Output for each Item Limited choice of input, response & evaluation types

Enter Desired Instruction, Question, Positive & Corrective Feedback for each Item

Optional file references for audio, Flash files, etc.

Assign Initial Learner Status: Known +, Unknown - or Undetermined ?

Layout Observable Interface Limited to positioning

Select TutorIT Configuration Options Limited Diagnostic & Instructional Options

Works with TutorIT

EZauthorShell Authoring System for Teachers

Page 17: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Statically Defined Knowledge Defining Input and Output Behavior in EZauthor

Page 18: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Statically Defined Knowledge Defining Problem Layout / Display Attributes in EZauthor

Page 19: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

What Does EZauthor Mean for Developers/Teachers/Trainers?

EZauthor “Short Cut” for Simple Tutors

Build adaptive tutors with absolutely no programming

Automatically concentrates on what learner does not know - e.g., learn a language more efficiently)

Page 20: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Leading CBI Developer Estimates* Development Times

Tutorials – 200 hours per 1 hour of instruction

Simulations, Games, Intelligent (adaptive) Tutors – 200-800 hours per 1 hour instruction

Non-interactive – 100 hours per hour of instruction

Allocation of Time 1. Front end analysis - 20%    2. Instructional Strategy Definition - 30%    3. Creation of Media and software assets - 20%    4. Integration of components and testing - 30%

* Foshay & Preese, Technology, Instruction, Cognition & Learning, 2005.

Page 21: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

Estimates with AuthorIT Development Times

Tutorials – 20-100 (vs. 200) hours per 1 hour of instruction

Simulations, Games, Intelligent (adaptive) Tutors – 20-400 (vs. 200-800) hours per 1 hour instruction

Non-interactive – 10 (vs. 100) hours per hour of instruction

Allocation of Time 1. Front end analysis – no change (20%)    2. Instructional Strategy Definition – (1% vs. 30%) 3. Creation of Media and software assets – no change (20%)    4. Integration of components and testing – estimate continuing

reductions as system matures to (5% vs. 30%)

Page 22: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

StatusReady for Classroom Testing

Arithmetic Skills Mathematical Processes

Ready for Authoring TutorIT Customizer tutorials show how to customize existing TutorIT tutorials EZauthor tutorials show teachers how to create simple

lessons AuthorIT tutorials show how to create professional

tutorials

Ready for Academic R&D Customize tutorials to test designer's favorite theory Easily develop tutors in designer's areas of expertise

copyright scandura 2001-5

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Page 23: Introduction to AuthorIT April 10, 2006 Symposium on Knowledge Representation TICL SIG Joseph M. Scandura, Ph.D. Chairman, Board Scientific Advisors, MERGE.

FREE Availability

TutorITFREELY download, install &/or distribute

>> www.scandura.com <<

Content, Customizers, EZauthor & AuthorITFREE to SIG TICL Members & Subscribers

Contact [email protected] for details

copyright scandura 2001-5-56


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