SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 1 The MATHESIS Ontology: Reusable Authoring Knowledge for Reusable Intelligent.

Slides:



Advertisements
Similar presentations
Language Technologies Reality and Promise in AKT Yorick Wilks and Fabio Ciravegna Department of Computer Science, University of Sheffield.
Advertisements

The Ontology Construction Problem Ontology construction requires the active engagement of domain experts Existing ontology authoring tools are not tailored.
Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.
Present by Oz Shapira.  User modeling ”is a sub-area of human–computer interaction, in which the researcher / designer develops cognitive models of human.
CHAPTER 13 Inference Techniques. Reasoning in Artificial Intelligence n Knowledge must be processed (reasoned with) n Computer program accesses knowledge.
Crew Scheduling Housos Efthymios, Professor Computer Systems Laboratory (CSL) Electrical & Computer Engineering University of Patras.
Chapter 22 Object-Oriented Systems Analysis and Design and UML Systems Analysis and Design Kendall and Kendall Fifth Edition.
An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 1 An Individualized Web-Based Algebra Tutor Based on Dynamic Deep Model Tracing Dimitrios.
Supporting (aspects of) self- directed learning with Cognitive Tutors Ken Koedinger CMU Director of Pittsburgh Science of Learning Center Human-Computer.
Selbo 2 SCORM Editor for eLearning Based on Ontologies Part of eLSE project Damyan Mitev University of Plovdiv “Paisii Hilendarski”
Object-Oriented Analysis and Design
Boolean Algebra Applications1 BOOLEAN ALGEBRA APPLICATIONS RELIABILITY OF CIRCUITS.
Using the Digital Anatomist Foundation Model: a Graphical User Interface Emily Chung Linda Shapiro, Dept. of Computer Science and Engineering University.
Projects March 29, Project Requirements Think Aloud –At least two people OR Difficulty Factors Assessment –Ideally >25 (at least one class), but.
The Semantic Web Week 13 Module Website: Lecture: Knowledge Acquisition / Engineering Practical: Getting to know.
Telescoping Languages: A Compiler Strategy for Implementation of High-Level Domain-Specific Programming Systems Ken Kennedy Rice University.
Sensemaking and Ground Truth Ontology Development Chinua Umoja William M. Pottenger Jason Perry Christopher Janneck.
Sharing Knowledge in Adaptive Learning Systems Miloš Kravčík Dragan Gašević Fraunhofer FIT, GermanySimon Fraser University, Canada
PDDL: A Language with a Purpose? Lee McCluskey Department of Computing and Mathematical Sciences, The University of Huddersfield.
AIMSA2010, Sep 10th 2010 "Ontology-Based Authoring of Intelligent Math Tutors ", D.Sklavakis & I. Refanidis 1 Ontology-Based Authoring of Intelligent Model-Tracing.
Protégé An Environment for Knowledge- Based Systems Development Haishan Liu.
Lesson 6. Refinement of the Operator Model This page describes formally how we refine Figure 2.5 into a more detailed model so that we can connect it.
Orion Overview. We build an internal model of the world, so we can predict future behaviour - we make the model out of active structure so it is interoperable.
KES 2011, Sep 13th 2011 “The MATHESIS Semantic Authoring Framework", D.Sklavakis & I. Refanidis 1 The MATHESIS Semantic Authoring Framework: Ontology-Driven.
Computer Science 240 Principles of Software Design.
ADL Slide 1 December 15, 2009 Evidence-Centered Design and Cisco’s Packet Tracer Simulation-Based Assessment Robert J. Mislevy Professor, Measurement &
Oakkar Fall The Need for Decision Engine Automate business processes Implement complex business decision logic Separation of rules and process Business.
SCORM By: Akshay Kumar. SCORM 2 What we want? What is SCORM? What is SCORM? Connection with e-learning Connection with e-learning Application of XML Technology.
31 st October, 2012 CSE-435 Tashwin Kaur Khurana.
CH07: Writing the Programs Does not teach you how to program, but point out some software engineering practices that you should should keep in mind as.
July 6, th International Protégé Conference Reasoning in a Tutoring System: Transforming Knowledge to Teaching. Olga Medvedeva Center for Pathology.
Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence.
1 USING EXPERT SYSTEMS TECHNOLOGY FOR STUDENT EVALUATION IN A WEB BASED EDUCATIONAL SYSTEM Ioannis Hatzilygeroudis, Panagiotis Chountis, Christos Giannoulis.
Compositional IS Development Framework Application Domain Application Domain Pre-existing components, legacy systems Extended for CD (ontologies) OAD Methods.
1 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Stuart Aitken Artificial Intelligence Applications.
Selenium Web Test Tool Training Using Ruby Language Discover the automating power of Selenium Kavin School Kavin School Presents: Presented by: Kangeyan.
Author: Lornet LD team Reuse freely – Just quote Desired Properties of a MOT Graphic Representation Formalism Simplicity and User Friendliness (win spec,
CISC474 - JavaScript 03/02/2011. Some Background… Great JavaScript Guides: –
Chapter 1 Introduction Dr. Frank Lee. 1.1 Why Study Compiler? To write more efficient code in a high-level language To provide solid foundation in parsing.
ITCS 6010 SALT. Speech Application Language Tags (SALT) Speech interface markup language Extension of HTML and other markup languages Adds speech and.
Agent Model for Interaction with Semantic Web Services Ivo Mihailovic.
DBSQL 14-1 Copyright © Genetic Computer School 2009 Chapter 14 Microsoft SQL Server.
Intelligent Database Systems Lab Presenter: WU, JHEN-WEI Authors: Rodrigo RizziStarr, Jose´ Maria Parente de Oliveira IS Concept maps as the first.
CSC 395 – Software Engineering Lecture 13: Object-Oriented Analysis –or– Let the Pain Begin (At Least I’m Honest!)
UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Semantic Web Services CS - 6V81 University of Texas at Dallas November.
Protégé as Professor: Development of an Intelligent Tutoring System With Protégé-2000 Olga Medvedeva Center for Pathology Informatics University of Pittsburgh.
An Ontological Framework for Web Service Processes By Claus Pahl and Ronan Barrett.
© 2005 Prentice Hall9-1 Stumpf and Teague Object-Oriented Systems Analysis and Design with UML.
Author: Gilbert Paquette Reuse freely – Just quote Meta-Knowledge Representation for Learning Systems (Part 1-What) Meta-Knowledge Representation for Learning.
Tutoring & Help System CSE-435 Nicolas Frantzen CSE-435 Nicolas Frantzen.
Computer Science and Software Engineering© 2014 Project Lead The Way, Inc. HTML5 Evolving Standards JavaScript.
Programming Tutoring Systems evaluation Boro Jakimovski Anastas Misev Institute of Informatics Faculty of Natural Sciences and Mathematics University “Ss.
PZ03BX Programming Language design and Implementation -4th Edition Copyright©Prentice Hall, PZ03BX –Recursive descent parsing Programming Language.
Steps to integrate XML How does XML processing work? Simple uses of passive DOM objects Adding behaviour to information A converter and translator subsystem.
1 The Software Development Process ► Systems analysis ► Systems design ► Implementation ► Testing ► Documentation ► Evaluation ► Maintenance.
George Goguadze, Eric Andrès Universität des Saarlandes Johan Jeuring, Bastiaan Heeren Open Universiteit Nederland Generation of Interactive Exercises.
Suggestions for Galaxy Workflow Design Using Semantically Annotated Services Alok Dhamanaskar, Michael E. Cotterell, Jessica C. Kissinger, and John Miller.
The International RuleML Symposium on Rule Interchange and Applications Visualization of Proofs in Defeasible Logic Ioannis Avguleas 1, Katerina Gkirtzou.
Think First, Code Second Understand the problem Work out step by step procedure for solving the problem (algorithm) top down design and stepwise refinement.
Copyright © Curt Hill Other Trees Applications of the Tree Structure.
CS 152: Programming Language Paradigms April 7 Class Meeting Department of Computer Science San Jose State University Spring 2014 Instructor: Ron Mak
Introduction to JavaScript MIS 3502, Spring 2016 Jeremy Shafer Department of MIS Fox School of Business Temple University 2/2/2016.
Find us at Have you ever wanted to start your own website or blog?
Inquiry learning and SimQuest
Programming Languages Translator
Presenter: Guan-Yu Chen
Program comprehension during Software maintenance and evolution Armeliese von Mayrhauser , A. Marie Vans Colorado State University Summary By- Fardina.
Subject: Language Processor
6.001 SICP Further Variations on a Scheme
Chapter 22 Object-Oriented Systems Analysis and Design and UML
Presentation transcript:

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 1 The MATHESIS Ontology: Reusable Authoring Knowledge for Reusable Intelligent Tutors Dimitrios Sklavakis and Ioannis Refanidis Department of Applied Informatics Univercity of Macedonia Thessaloniki GREECE

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 2 Overview The MATHESIS Project  Bottom-up approach  The MATHESIS Algebra Tutor Tutor Representation in MATHESIS Ontology  The OWL-S process model  The Tutoring model  The Authoring model  The Program code model  The Interface model Further Work Discussion

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 3 The MATHESIS Project Approach: Bottom – Up Ontological Engineering The MATHESIS Algebra/Math Tutor(s): Declarative and Procedural Knowledge hard-coded in HTML and JavaScript The MATHESIS Ontology: Declarative description of the User Interface, Domain Model, Tutoring Model, Student Model and Authoring Model( OWL and OWL-S) The MATHESIS Authoring Tools: Guiding Tutor Authoring Through Searching in the Ontology and “Interpreting” the Authoring Model (OWL-S Processes) Domain Experts’ Knowledge: Domain + Tutoring + Assessing + Programming

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 4 The MATHESIS Algebra Tutor Web-based  User Interface: HTML + JavaScript  Specialized math editing applets: WebEq by Design Science Declarative Knowledge: JavaScript variables and Objects Procedural Knowledge: JavaScript functions Domain cognitive model  Top-level skills (20) : algebraic operations (7), identities (5), factoring (8)  Detailed cognitive task analysis gives a total of 104 cognitive (sub)skills  Detailed hint and error messages for all of the above

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 5 MATHESIS Algebra Tutor Screenshot Help, Hint and Error Messages Area WebEq Input Control for the Algebraic Expression being Rewriten WebEq Input Control for Student Answers WebEq Input Control for Intermediate Results

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 6 The OWL-S Process Model: Ontological Representation of Procedural Knowledge A composite process is a tree whose non-terminal nodes are control constructs Leaf nodes are invocations of other processes, composite or simple (Perform constructs) In MATHESIS Ontology, procedural knowledge is represented as composite processes

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 7 Representing the Tutoring Model: The Model-Tracing Process(KVL variation) Being procedural knowledge… …the model- tracing algorithm is represented as a composite porcess… …calling other composite processes for each tutoring task.

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 8 Representing the Authoring Model: “Interpreting” the authoring processes The Model-Tracing process The Execute_Task_By_Expert authoring process The define_data_structures_for_knowledge_components authoring process For each tutoring task… There is an authoring process… …which can be further refined.

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 9 From processes to code: monomial multiplication var pos; var i; var vars1 = parsedMonomials[0].variables; var vars2 = parsedMonomials[1].variables.concat([]); var n1 = vars1.length; var n2 = vars2.length; var exps1 = parsedMonomials[0].exponents; var exps2 = parsedMonomials[1].exponents; for(i=0; i < n1 ; i++) { parsedMonomials[2].variables.push(vars1[i]); pos = getVariablePosition(vars1[i],vars2); if(pos == -1) { parsedMonomials[2].exponents.push(exps1[i]); var sum = exps1[i]; } else { var sum = parseInt(exps1[i]) + parseInt(exps2[pos]); parsedMonomials[2].exponents.push(sum); vars2[pos] = ""; } for(var j=0; j < n2; j++) { if(vars2[j] != "") { parsedMonomials[2].variables.push(vars2[j]); parsedMonomials[2].exponents.push(exps2[j]); } } Part of the model-tracing process adapted to monomial multiplication The monomial_multiplication_execution process Atomic processes are JavaScriptStatement individuals JavaScript program lines are JavaScriptProgramLine individuals hasJavaScriptCode hasJavaScriptStatement

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 10 The Low-Level Ontology: JavaScript Code Representation JavaScript code is represented as a special kind of atomic process, the JavaScriptStatement Every JavaScriptStatement has a corresponding JavaScript_ProgramLine … …which holds the actual JavaScript code

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 11 The Low-Level Ontology: Interface Representation

Interface Representation …which defines corresponding HTMLObject(s). Every line of HTML code is represented as an HTML_ProgramLine… HTMLObject(s) are connected via their hasFirstChild and hasNextSibling properties to represent the DOM

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 13 The MATHESIS Ontology Further Work Extend, Refine, Formalise the Ontology Represent the Algebra Tutor in the Ontology Create Authoring Tools:  Parsers HTML ↔ MATHESIS Interface model  Parsers JavaScript ↔ JavaScriptStatements  Interpreter (“tracer”) for the OWL-S processes  Visualisation Tools for the authoring processes and the authored tutor parts (tutoring, domain, student models, interface and program code)

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 14 The MATHESIS Ontology Discussion Being an Ontology, it has all known advantages and disadvantages of ontologies New approach: ontological representation of procedural knowledge (rules) through OWL-S processes. Both authoring and authored knowledge share the same representation and lie in the same place Newly authored tutors become new knowledge to be used for the next ones Maximum knowledge reuse anticipated

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 15 Thank you! You May Visit The MATHESIS Algebra Tutor Interactive Event at 7pm

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 16 Representing the Authoring Model: The define_data_structures_for_knowledge_components authoring task process

SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 17 Representing the Authoring Model: The Task_Execution_By_Expert authoring task process