Educational Technologies WS2006
Dr. habil Erica Melis
ActiveMath- Group
Deutsches Forschungszentrum
für Künstliche Intelligenz (DFKI)
Source: Erica Melis
Educational Technologies
About the Field and the Course
• Intelligent assistent systems for learning
– components of ITSs
– AI-techniques and related ones
• Practical applications
• Interdisciplinary and empirically validated
• Learn actively!!!
• Test described software on Web if available
– Make suggestions yourself
• Hands-on experience and authoring in projects
Source: Erica Melis
Educational Technologies
Scheme of the Course
• http://www.activemath.org/teaching/edtechsws0607
– register with Matrikelnummer
• Projects
– Start as soon as possible
• Author interactive script in ActiveMath
– Inform george@activemath.org about groups til end of
week
– Not everything on the slides…
Source: Erica Melis
Educational Technologies
Approximate Plan of the Course
18.10. Introduction and overview
25.10. Introduction to ActiveMath
XML- Knowledge Representation
8.11. Student Modelling
15.11. Web technologies and security
22.11. Tutorial Planning and instructional design
29.11. Media Principles
6.12. Interactive exercises
13.12. Authoring tools, CTAT
20.12. Diagnosis: model tracing and domain reasoning
10.1.
17.1.
24.1.
31.1.
7.2.
14.2.
Diagnosis: constraint based
Tutorial dialogues
Action analysis and ML techniques
Cognitive tools
Meta-cognitive support
student projects
Source: Erica Melis
Educational Technologies
Why Technology-Enhanced Learning ?
• Independent of time & place
• Individual tutoring
• better learning (modalities
visualization..)
• (Semi)-automatic assessment
• Information for teachers
• cost effective
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Distance learning
Virtual Universities
Training on the job
Military training
Training for disabled
• Knowledge resources from
the Web
Source: Erica Melis
Educational Technologies
Data1：From “Statistics Bulletin on Economic
and Social Development in P.R.China 2004 ”
Admission Proportion for High Education
19% 4,200,000
Admitted
Not Admitted
81% 17,905,000
Source: Erica Melis
Educational Technologies
Data 2： From CNNIC (China Network Information Center)
“Statistic Report on the Development Status of Network in China ”
Million Person
Increment Rate 18.9%
94M
10 0
90
80
70
60
50
40
30
20
10
0
79M
199M in America
From ComScore
69M
45.8M
January
July
26.5M
16.9M
2000
2002
2004
The only purpose of 8.4% (7.89 million) users going online is for education
21.3% users prefer more educational information, 20 million broadband users
Source: Erica Melis
Educational Technologies
History: First Generation of Tutors, CAI
1960ies: Programmed instruction 1970ies: CAI.. PLATO, SHIVA
• IF
the
correct response THEN present new element ELSE goto
• Computer-Aided Instruction (CAI) or CAL
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store and retrieve data, exercise bank with answers
pre-defined branches of problem solving
no ‚understanding‘ of problems, few anticipated wrong answers
Independent of student‘s understanding, preferenes, behaviour
linear (not individualized) progression of instruction
no diagnosis of errors
Source: Erica Melis
Educational Technologies
History: Second Generation of Tutors, ITS
1970ies…Scholar
[Carbonell,Brown]
• Internal domain representation
knowledge base
• Problem solver, inference engine
(XPS)
-> cause of errors
-> more appr. response
• Exercise bank
• Limited dialogue and QA
Source: Erica Melis
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1990ies…PACT
[Anderson]
Student modeling
Domain Expert Module
Model learners‘ errors
Tutoring module (intervention
modalities)
• Ped+cognitive theories developed
• more autonomous student
• Lab- and realistic evaluations
• bandwidth of user interface,
– more variety of responses
– more interaction
Educational Technologies
CAI…ITS Architectures
1
Exercise
bank
Knowledge
Base
Expert system
User interface
Source: Erica Melis
selector
Educational Technologies
Exercise
bank
History: Third-Generation Learning Systems
• More student modeling:
emotional, motivational, affective, situational
learning from massive log data
• Natural language tutorial dialogues
• Explorative, interactive, inquiry learning
• Collaborative learning
• Support of meta-cognition
• Web-based systems
• Multimedia and (adaptive) hypermedia based on pedagogy
• Semantic knowledge representation (semantic web)
• Retention tests, social skills, performance/learning
• AHA, Tectonica, ActiveMath, ELM-ART, Edutella, Wayang Outpost,
iHelp, Algebra Cognitive Tutor, BEETLE, Help Tutor
Source: Erica Melis
Educational Technologies
A Generic ITS Architecture
Intelligent Tutorial Component
Student
Graphical user
interface
Domain
KR
Problem
Solver
Solution
Graph
Action
Interpreter
Interaction
History
Problem
Selector
Student Model
Solution
Evaluator
Feedback
Generator
Source: Erica Melis
Educational Technologies
Curriculum
Planner
Curriculum
Andes Architecture
Authoring Environment
Graphical author
interface
Student Environment
Workbench
Problem
Presentation
Assessor (BN)
Physics
Rules
Problem
Definition
Physics
Problem
Solver
Solution
Graph
Action
Interpreter
Help System
Procedural
help
Source: Erica Melis
Educational Technologies
Student Model
tutoring strategy
Conceptual
help
Example
study help
ActiveMath MVC Architecture
Source: Erica Melis
Educational Technologies
Some Intelligent Systems
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Cognitive Tutors (Koedinger et al)
ELM-ART (Weber et al)
Andes, Atlas-Andes (vanLehn et al)
Cabri-Geometre (Balacheff et al)
Wayang Outpost (Wolff,Arroyo,Murray)
ActiveMath: www.activemath.org (Melis et al)
Belvedere (Suthers)
I-Help (Greer et al)
Tectonica, AHA (Murray et al, deBra+Aroyo)
AutoTutor, BEETLE (Graesser et al, Moore et al)
Help-Tutor (Aleven, Koedinger)
Source: Erica Melis
Educational Technologies
Interdisciplinary Field
AI
CoLinguistics
TEL
Cognitive*
Psychology
Web-Technology
Multimedia
Pedagogy
Source: Erica Melis
Content
Educational Technologies
Contributions of AI
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Knowledge representation
User modelling
Intelligent user interfaces
Presentation planning, intelligent sequencing
Diagnosis
Data mining, Machine Learning
Problem solving systems/automated reasoning
Agent-based (help) systems
Adaptive hypermedia
Source: Erica Melis
Educational Technologies
AI: User modeling
• Bayesian nets
Probability distribution
events, causes, evidences
conditional dependences
diagnostic/causal update
Source: Erica Melis
Educational Technologies
AI: Knowledge Representation
Frames in Cognitive Tutors
Problem WME:
(make-wme composed-cen-insc
isa problem
key-quantities (angle-KHP-measure arc-KP-measure angle-KQP-measure)
key-reasons (angle-KHP-measure ...)
questions (question1)
given-relational-quantities (central-angle-KHP inscribed-angle-KQP)
table composed-cen-insc-table
)
Relation WME... inscribed-angle...
inputs (arc-KP-measure)
output angle-KQP-measure
Quantity WME ... angle-KHP-measure...unit..dimension..labels..
Source: Erica Melis
Educational Technologies
AI: Knowledge Representation
– Semantic networks
– DAML/OIL/OWL decision logics for
XML-Representation
– Meta data (publ, mathematical, pedagogical)
Ontology
Source: Erica Melis
Educational Technologies
Web-Languages and Technologies
Standardization!!!
• IEEE LTSC, LOM
• IMS Global Learning
Consortium
– Apple
– Cisco
– IBM
– Microsoft
– Sun
– WebCT
– Universities
– ….
• Open e-Book
Source: Erica Melis
• Meta data
• Interoperability of services
• Interoperabilty of content
(ontologies)
• Architectures
• Presentation of content
• Wiki
• Security
Educational Technologies
Contributions from Pedagogy
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Goals
Content sequence
Strategies, Methods
Media, tools
Competencies
difficulty
Szenarien, Feedback
exercises
Handling errors
Frequent mistakes
Feedback
Multiple solutions
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Didactic
Socratic
Inquiry
Discovery
LearnNew
Rehearse
Collaborate
MultiMedia
user modeling
(competencies)
Source: Erica Melis
Educational Technologies
Bloom: taxonomy of educational objectives
Knowledge
recall information...
describe, identify, name, who, when, where
Comprehension grasp meaning,understand info
summarize, contrast,associate, explain
Application
Use information..in new situations
apply, calculate, complete, prove, modify
Analysis
see patterns,identify components
analyze, order, infer, select
Synthesis
generalize, predict, create new ideas
combine, plan, invent, generalize
Source: Erica Melis
Educational Technologies
PISA Competencies
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Compute
Apply
Model
Argue
Solve problem
Collaborate
Use tools
Meta-cognition…
Source: Erica Melis
Educational Technologies
Contributions Cognitive Psychology
• Behaviourisms vs. constructivisms [Piaget, Vygotski]
• Feedback
– motivation: personalized, self-guided, social, active
[Decy&Ryan...]
– zone of proximal development [Vygotsky]
– gender-specific
– meta-cognition [White…]
– adaptive support [Mandl...]
– multi-modality
• structured presentation of solutions [Catrambone]
Effective design vs on-line book with animations
Source: Erica Melis
Educational Technologies
Cognitive Psychology: Multimedia Learning
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Source: Erica Melis
Multimedia Principle
Integration Principle
Modality Principle
Redundancy P.
Coherence Principle
Personalization P.
Learner control
Educational Technologies
Cognitive Psychology: some results
– Self-explanation of worked-out examples
[Renkl,Chi,Merrinboer,Siegler]
– Why does tutorial dialog help? [Chi etal 2001]
• even if human tutors don‘t know tutoring
• no-content prompts
• ask, don‘t tell ?
• students own communication?
– Learning from errors/impasses only (?)
– Conceptual change (Vosniadou)
– Influence of motivation, self-efficiacy [Bandura]
– Evaluation of systems
Source: Erica Melis
Educational Technologies
Conclusion
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Pursue learning
Learn actively and believe in yourself
Ask questions if you don‘t understand
Discover the world of research
Source: Erica Melis
Educational Technologies
Student Projects
1.Visualization of the pedagogical knowledge domain
Analyze and visualize the structure of pedagogical tasks
2. SLOPERT exercise generator
Explore the problem space and create a ActiveMath exercises.
3. Learner Model for iCMap
Catch and analyze events generated by iCMap
4. Domain Viewer:
Render an ActiveMath domain (concepts, relations)
5. Exercise generation with extended randomizer
to support intervals and (adaptive) randomizing over a set of
elementary functions and their compositions
Source: Erica Melis
Educational Technologies
Student Projects
6. Mathematical Rendering Tester:
Support authors by rendering mathematical formulae on
the fly
7. Analyzing Online Collaborative Data
Generate Machine Learning classifiers from log data
8. E-Portfolio Viewer
Implement an interactive viewer for the IMS eP Spec
Source: Erica Melis
Educational Technologies