An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 1 An Individualized Web-Based Algebra Tutor Based on Dynamic Deep Model Tracing Dimitrios.

Slides:



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

Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.
What can CTAT do for you? Overview of the CTAT track Vincent Aleven, Bruce McLaren and the CTAT team 3rd Annual PSLC LearnLab Summer School Pittsburgh,
Educational data mining overview & Introduction to Exploratory Data Analysis Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction.
Improving learning by improving the cognitive model: A data- driven approach Cen, H., Koedinger, K., Junker, B. Learning Factors Analysis - A General Method.
Case Tools Trisha Cummings. Our Definition of CASE  CASE is the use of computer-based support in the software development process.  A CASE tool is a.
Supporting (aspects of) self- directed learning with Cognitive Tutors Ken Koedinger CMU Director of Pittsburgh Science of Learning Center Human-Computer.
SWEL09, July 7th 2009 "The MATHESIS Ontology", D.Sklavakis & I. Refanidis 1 The MATHESIS Ontology: Reusable Authoring Knowledge for Reusable Intelligent.
Cognitive Tutors ITS, Sept 30, Overview Production system models –For LISP, geometry, and algebra 8 principles from ACT theory Gains: 1/3 time to.
IIS Seminar Computer Human Interaction: Improving Computing for Novice Programmers Cheryl Seals Auburn University Computer Human Interaction Laboratory.
Cognitive Processes PSY 334 Chapter 8 – Problem Solving May 21, 2003.
Providing Tutoring Service through Accumulating Interaction Data Chi-Jen LIN Fo Guang University, Taiwan.
AIMSA2010, Sep 10th 2010 "Ontology-Based Authoring of Intelligent Math Tutors ", D.Sklavakis & I. Refanidis 1 Ontology-Based Authoring of Intelligent Model-Tracing.
1/1/ Designing an Ontology-based Intelligent Tutoring Agent with Instant Messaging Min-Yuh Day 1,2, Chun-Hung Lu 1,3, Jin-Tan David Yang 4, Guey-Fa Chiou.
An Integrated Solution for Web-based Mathematical Expression Inputting Wei Su Department of Computer Science, Lanzhou University, PRC Department of Computer.
Programming Fundamentals (750113) Ch1. Problem Solving
KES 2011, Sep 13th 2011 “The MATHESIS Semantic Authoring Framework", D.Sklavakis & I. Refanidis 1 The MATHESIS Semantic Authoring Framework: Ontology-Driven.
Intervention Resource Guide. Math Intervention Courses Address foundational math skills – Whole numbers – Addition, Subtraction, Multiplication, Division.
Megha Cynthia Shyam Hannah Jen. What is it? Carnegie Learning, Inc. Math curriculum Cognitive tutor is software For middle & high schools as well as homeschooling.
31 st October, 2012 CSE-435 Tashwin Kaur Khurana.
Building Intelligent Tutoring Systems with the Cognitive Tutor Authoring Tools (CTAT) Vincent Aleven and the CTAT team 7th Annual PSLC Summer School Pittsburgh,
Beverly Park Woolf University of Massachusetts/Amherst U.S.A
Ryann Kramer EDU Prof. R. Moroney Summer 2010.
Tutoring and Learning: Keeping in Step David Wood Learning Sciences Research Institute: University of Nottingham.
MASTERS THESIS DEFENSE QBANK A Web-Based Dynamic Problem Authoring Tool BY ANN PAUL ADVISOR: PROFESSOR CLIFF SHAFFER JUNE 2013 Computer Science Department.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
1 USING EXPERT SYSTEMS TECHNOLOGY FOR STUDENT EVALUATION IN A WEB BASED EDUCATIONAL SYSTEM Ioannis Hatzilygeroudis, Panagiotis Chountis, Christos Giannoulis.
Educational Courseware Created by Dr. Patty LeBlanc Stetson University.
Quincy BrownKallen Tsikalas Research Questions & Hypotheses Theoretical Assumptions: Good, Bad & Ugly Using CTAT to test hypotheses The Interface Beneath.
Introduction to the Cognitive Tutor Authoring Tools (CTAT) and Example-Tracing Tutors Bruce McLaren Systems Scientist, Co-Manager of the CTAT Project Team.
Design Process for Web Applications Authors :Lorna Uden Source : IEEE MultiMedia, vol. 9, no. 4, 2002, pp Speaker :Li-Ya Liao Adviser : Ku-Yaw Chang.
Learning SQL with a Computerized Tutor (Centered on SQL-Tutor) Antonija Mitrovic (University of Canterbury) Presented by Danielle H. Lee.
หลักการโปรแกรม เพื่อแก้ปัญหาโดยใช้คอมพิวเตอร์
CSA3212: User Adaptive Systems Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 9: Intelligent Tutoring Systems.
Tuteurs cognitifs: La théorie ACT-R et les systèmes de production Roger Nkambou.
1 The Software Development Process  Systems analysis  Systems design  Implementation  Testing  Documentation  Evaluation  Maintenance.
Simulated Student: Building Cognitive Model by Demonstration Noboru Matsuda School of Computer Science Carnegie Mellon University.
Measuring What Matters: Technology & the Assessment of all Students Jim Pellegrino.
By Wayne Sibley Information Engineering Technology University of Cincinnati College of Applied Science.
Software Development Process.  You should already know that any computer system is made up of hardware and software.  The term hardware is fairly easy.
Noboru Matsuda Human-Computer Interaction Institute
1 USC Information Sciences Institute Yolanda GilFebruary 2001 Knowledge Acquisition as Tutorial Dialogue: Some Ideas Yolanda Gil.
Tutoring & Help System CSE-435 Nicolas Frantzen CSE-435 Nicolas Frantzen.
Vincent Aleven & Kirsten Butcher Robust Learning in Visual/Verbal Problem Solving: Contiguity, Integrated Hints, and Elaborated Explanations.
SimStudent: A computational model of learning for Intelligent Authoring and beyond Noboru Matsuda Human-Computer Interaction Institute Carnegie Mellon.
COMM89 Knowledge-Based Systems Engineering Lecture 8 Life-cycles and Methodologies
The Software Development Process
711: Intelligent Tutoring Systems Week 1 – Introduction.
Introduction to Prolog. Outline What is Prolog? Prolog basics Prolog Demo Syntax: –Atoms and Variables –Complex Terms –Facts & Queries –Rules Examples.
 Programming - the process of creating computer programs.
SimStudent: Building a Cognitive Tutor by Teaching a Simulated Student Noboru Matsuda Human-Computer Interaction Institute Carnegie Mellon University.
Data mining with DataShop Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction & Psychology Carnegie Mellon University.
RULES Patty Nordstrom Hien Nguyen. "Cognitive Skills are Realized by Production Rules"
Mass Producing Example- Tracing Tutors Bruce McLaren Human-Computer Interaction Institute Carnegie Mellon University.
Of An Expert System.  Introduction  What is AI?  Intelligent in Human & Machine? What is Expert System? How are Expert System used? Elements of ES.
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.
Artificial Intelligence
Teacher Directed Instruction. Use for teaching basic facts, knowledge, and skills (examples): New tasks Alphabetizing Unfamiliar material Science equations.
Tutoring & Help Systems Deepthi Bollu for CSE495 10/31/2003.
Does adaptive scaffolding facilitate students ability to regulate their learning with hypermedia? 指導教授:陳明溥 學 生 :王麗君.
Inquiry learning and SimQuest
Presenter: Guan-Yu Chen
H5P: Using an Interactive Assessment Tool in Moodle
G.W. CARVER MIDDLE EOC MATHEMATICS WORKSHOP October 25, 2017
EOC MATHEMATICS WORKSHOP
CS 1302 Programming Principles II
The essentials of Learning, E-learning and ISD
Simulated Student: Building Cognitive Model by Demonstration
Mike Timms and Cathleen Kennedy University of California, Berkeley
Presentation transcript:

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 1 An Individualized Web-Based Algebra Tutor Based on Dynamic Deep Model Tracing Dimitrios Sklavakis and Ioannis Refanidis Department of Applied Informatics Univercity of Macedonia Thessaloniki GREECE

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 2 Outline The MATHESIS Project Introduction: Cognitive Tutors Motivation: Cognitive Tutors Successful Paradigm Goals: Authoring Tools for Cognitive Tutors Research approach: Bottom - Up The MATHESIS Algebra Tutor Web-based Deep Cognitive Model Tracing Broad Knowledge Monitoring Related Work Cognitive Tutor Authoring Tools (Carnegie Mellon ) Future Work Ontology Authoring Tools

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 3 The MATHESIS Project Cognitive Tutors Motivation Goals Research approach

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 4 The MATHESIS Project Cognitive Tutors Model-tracing ITS build at Carnegie Mellon University Learning by Doing: Problem-solving environment with interactive tools Step by step tutorial guidance with feedback messages (correct, error, hints) Can handle multiple solution paths Adaptive problem selection and student pacing

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 5 The MATHESIS Project Cognitive Tutors and the ACT-R theory Adaptive Control of Thought-Rational:  Cognitive Theory of Learning and Performance  Learning by doing not by watching and listening Cognitive Model Based on the ACT-R theory:  Problem solving knowledge is made of cognitive skills  A cognitive skill consists of: Procedural knowledge: IF…THEN production rules Declarative knowledge: Facts consisting of property-value pairs

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 6 Cognitive Tutor Technology: Use ACT-R theory to individualize instruction 3(2x - 5) = 9 6x - 15 = 92x - 5 = 36x - 5 = 9 Cognitive Model: A system that can solve problems in the various ways students can If goal is solve a(bx+c) = d Then rewrite as abx + ac = d If goal is solve a(bx+c) = d Then rewrite as abx + c = d If goal is solve a(bx+c) = d Then rewrite as bx+c = d/a Model Tracing: The tutor matches the student’s steps against the solution produced by the cognitive model → context-sensitive instruction Known = 85% Known =45% Bug message: You must also multiply a by c Hint: You must distribute a over bx and c Knowledge Tracing: The tutor records cognitive skill learning from problem to problem → individualized activity selection and pacing

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 7 The MATHESIS Project Motivation: Cognitive Tutors’ Real-world Success Algebra Cognitive Tutor in over schools in the USA, students per year. Geometry Cognitive Tutor in 350 schools Approved by the U.S. Dept. of Education Full year classroom experiments show significant efficiency gains:  % better on problem solving & representation use.  15-25% better on standardized tests.

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 8 The MATHESIS Project Goal: Authoring Tools for Math Cognitive Tutors Development costs of instructional technology are high  Approximately 300 development hours per hour of instruction for Computer Aided Instruction Cognitive Tutors:  Approximately 200 development hours per hour of instruction  Requires PhD level cognitive scientists and AI programmers Solution: Easy to use Cognitive Tutor Authoring Tools

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 9 The MATHESIS Project Approach: Bottom – Up Ontological Engineering The MATHESIS Algebra/Math Tutor(s): Declarative and Procedural Knowledge hard-coded in a programming language The MATHESIS Ontology: Declarative description of the User Interface, Domain Model, Tutoring Model, Student Model and Authoring Model The MATHESIS Authoring Tools: Guiding Tutor Authoring Through Searching in the Ontology Domain Experts’ Knowledge: Domain + Tutoring + Assessing + Programming

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 10 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

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 11 The MATHESIS Algebra Tutor Tutoring model: deep cognitive model tracing through knowledge reuse When tutoring a cognitive skill, e.g. polynomial-multiplication the tutor traces the cognitive model for each one of the monomial-multiplications Student model: broad knowledge monitoring  The tutor records and timestamps in a database the student’s performance for each skill that is tutored, giving a percentage assessment of cognitive skill learning over time  The tutor records in a database all the student’s interactions with the interface so that they can be re-traced at any time

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 12 MATHESIS Algebra Tutor Demo

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 13 Related Work CMU Cognitive Tutor Authoring Tools Example-tracing tutors:  Built through “programming by demonstration”  Authors create Examples of how the students should solve specific problems  For each solution step the author enters the answer Cognitive Tutors  Built through Cognitive Task Analysis  Authors create Cognitive Models of how the students should solve a range of problems  For each solution step the author enters production rules CTAT mainly supports Example-tracing Tutors

An Individualized Web-Based Algebra Tutor D.Sklavakis & I. Refanidis 14 Future Work Ontological Engineering  Build a declarative description of the Algebra Tutor’s knowledge (Interface, Domain, Tutoring and Student models)  Build an Authoring Model through Cognitive Task Analysis of the Algebra Tutor creation Authoring Tools  Search, Select, Modify the existing Ontology → Re- create (part of ) the existing Algera Tutor  Extend the Ontology → Create new Tutors!