recognizing opportunities for mixed-initiative interactions based on the principles of...

Recognizing Opportunities for Mixed-Initiative Interactions based on the

Principles of Self-Regulated Learning

Jurika Shakya, Samir Menon, Liam Doherty, Mayo Jordanov, Vive KumarNovember 6, 2005

Simon Fraser University

AAAI-2005 Fall Symposia, Arlington, Virginia

2Outline

Outline

Motivation Self Regulatory Learning Theory Example MI-EDNA Architecture Future Direction

3Motivation

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

The top performers are associated with self-regulatory capabilities.

Learners in the opposite end of the bell curve, could improve with some help in their learning style.

The goal of helping the learners learn with SRL theory-centric help can be best achieved through mixed-initiative approach.

Learner ScoreN

umbe

r of

lear

ners

Top Performers

Learner that can use help

in self regulating

their learning

4SRL

Self-Regulatory Learning Theory SRL is a theory that concerns how learners develop

learning skills and how they develop expertise in using learning skills effectively.

SRL theories Zimmerman’s 3 phase model

Forethought Phase Performance Phase Self-reflection Phase

Winne’s 4 state model Knowledge Goals Tactics and Strategies Product

5SRL

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

6Example

Interactions

SRL guidance

MI-EDNA

7MI-EDNA Architecture

MI-EDNA System Architecture

HTTS.owlRules

CILT-Instantiated.owl

Facts

Inference Engine

Iden

tifie

d In

itiat

ive

Inte

ract

ion

(from

S

yste

m)

User

Inte

ract

ion

initia

ted

(by

user

)

Query Tool

Log_file.XMLXML Parser

CILT.owlInstantiator

CILT-Instantiated.owl Query Tool

(e.g. Protégé)

8Recognize MI

Recognition of Initiative Opportunities passively observes learner interactions

Instantiating the interactions into the CILT ontology recognizes opportunities for initiatives

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. actively initiates interactions

Based on the SRL principles Based on the scaffolding/Fading principles

DoThink

React

NoticeKnow

RegulateRecognizeRepresent

9recognize opportunities

Recognize Opportunities

JESSTranslator

OWL File(Instantiated

Domain Ontology)XSLT

(Owl2Jess)JESS FACTS

XSLT (Owl2Jess)

OWL File(Rules in SWRL) JESS RULES

Chat InterfaceJESS OUTPUT

QUERY JESS INPUT

10Outline

Actively Initiates Dissemination Categories

Content Scaffolds are based on the content that the learner is currently interacting within a session.

Process Scaffolds guide the learner to monitor his/her learning processes.

Learner Knowledge Scaffolds are based on the subject knowledge of the learner as modeled by the system.

Normative Scaffolds 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 system provides relevant information when it is aware of the information required by a

learner in response to his/her interactions.

11Outline

Future workSome of the mixed-initiative aspects of this research is to Explore the suitable interfaces required for mixed-

initiative 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.

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 LearningKit project (SSHRC-INE)

LORNET project (NSERC)