Transcript AAAI0507 - George Mason University

Recognizing Opportunities for MixedInitiative Interactions based on the
Principles of Self-Regulated Learning
Jurika Shakya, Samir Menon, Liam Doherty, Mayo Jordanov, Vive Kumar
November 6, 2005
Simon Fraser University
AAAI-2005 Fall Symposia, Arlington, Virginia
Outline
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Motivation
Self Regulatory Learning Theory
Example
MI-EDNA Architecture
Future Direction
Outline
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Motivation
Learning is viewed as an activity that
students do for themselves in a
proactive way rather than as a covert
event that happens to them in reaction
to teaching
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The top performers are associated
with self-regulatory capabilities.
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Learners in the opposite end of the
bell curve, could improve with some
help in their learning style.
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The goal of helping the learners learn
with SRL theory-centric help can be
best achieved through mixed-initiative
approach.
Motivation
Top
Performers
Number of learners
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Learner that
can use help
in self
regulating
their learning
Learner Score
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Self-Regulatory Learning Theory
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SRL is a theory that concerns how learners develop
learning skills and how they develop expertise in
using learning skills effectively.
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SRL theories
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Zimmerman’s 3 phase model
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Winne’s 4 state model
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SRL
Forethought Phase
Performance Phase
Self-reflection Phase
Knowledge
Goals
Tactics and Strategies
Product
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Self-Regulatory Learning Theory
Phases and Subprocesses of Self-Regulation. From B.J. Zimmerman and M. Campillo (in press), “Motivating Self-Regulated Problem
Solvers.” In J.E. Davidson and Robert Sternberg (Eds.), The Nature of Problem Solving. New York: Cambridge University Press
SRL
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SRL guidance
Interactions
MI-EDNA
Example
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MI-EDNA System Architecture
Log_file.XML
XML Parser
CILTInstantiated.owl
Query Tool
(e.g. Protégé)
CILT.owl
Instantiator
Rules
HTTS.owl
Inference Engine
Int
e
ra c
tio
n
ini
tia
ted
( by
us
er)
Facts
Identified Initiative
Interaction (from
System)
CILTInstantiated.owl
Query Tool
User
MI-EDNA Architecture
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Recognition of Initiative Opportunities
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passively observes learner interactions
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Instantiating the interactions into the CILT ontology
recognizes opportunities for initiatives
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Tracking interactions into learning tasks
 Mapping the learning tasks into tactics and strategies
 Inferring the activities involved in the SRL phases from the tactics and
strategies.
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actively initiates interactions
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Based on the SRL principles
 Based on the scaffolding/Fading principles
Recognize MI
Know
Notice
Think
Do
Represent
Recognize
Regulate
React
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Recognize Opportunities
OWL File
(Instantiated
Domain Ontology)
XSLT
(Owl2Jess)
JESS OUTPUT
Chat Interface
JESS FACTS
Translator
OWL File
(Rules in SWRL)
recognize opportunities
XSLT
(Owl2Jess)
JESS
QUERY JESS INPUT
JESS RULES
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Actively Initiates
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Dissemination Categories
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Content Scaffolds
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Process Scaffolds
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place their emphasis on the norms established by other learners in group-study or
class-room settings. The feedback offered here is expected to help a learner learn by
emulating the tactics of others.
Context Scaffolds
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Outline
are based on the subject knowledge of the learner as modeled by the system.
Normative Scaffolds
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guide the learner to monitor his/her learning processes.
Learner Knowledge Scaffolds
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are based on the content that the learner is currently interacting within a session.
system provides relevant information when it is aware of the information required by a
learner in response to his/her interactions.
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Future work
Some of the mixed-initiative aspects of this research is to
 Explore the suitable interfaces required for mixedinitiative aspect of MI-EDNA
 An evaluation of the influence of mixed-initiative
interactions and interfaces
 Explanation-aware SRL modelling and scaffolding/fading
techniques
 The effects of MI approach SRL help on the learner
 Deploying the MI-EDNA system on various other
domains.
Outline
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THANK YOU
Questions ?
MI3 Team, SFU
(Liam Doherty, Mayo Jordanov, Sam Menon, Shilpi Rao, David Brokenshire, Pat Lougheed,
Vive Kumar)
This research was funded by
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LearningKit project (SSHRC-INE)
LORNET
project (NSERC)