Personalised Support for Reflective Learning in Fire Risk Assessment Wichai Eamsinvattana Supervisors: Vania Dimitrova, School of Computing David Allen,

Personalised Support for Reflective Learning

in Fire Risk Assessment

Wichai Eamsinvattana

Supervisors: Vania Dimitrova, School of Computing David Allen, Business School

Presentation Plan

• Motivation: Reflective Learning for Fire Risk Assessment

• Goal and Research Questions

• PORML Framework

• Contribution

• Conclusions

• Training needs– Lack of effective training models in Fire and Rescue

Services (FRS)

• Importance of risk assessment skills– Crew commanders’ risk assessment skills have major

impact on the efficiency and effectiveness of dealing with fire accidents

• Need for personalisation– Crew commanders come from diverse backgrounds,

have different experience, frequently changing jobs, and often do not have enough practice

Motivation

Disconnected from the real context

–Simulated environment–Off-site–Not very effective

Training at Fire and Rescue Services

Conditions rapidly change and various aspects (e.g. road traffic, lethal chemicals)

Previous experience is important

Crew commander’s risk assessment is crucial

Many crew commanders are inexperienced

Review of the risk assessment normally takes a lot of time (a month, 3 months or more)

(interviews with UK FRS representative)

What Happens in Reality

Problem: Can we find intelligent ways to capture the real risk assessment activities and create appropriate learning scenarios?

Our Approach: Personalised mobile reflective learning

The Problem & Our Approach

Mobile technology to support decision making at FRS

Aimed at decision makingbut learning is not considered

Virtual reality to support reflective learning

Learning by reflecting on FRS activities but disconnected from the real environment

No research has been conducted to use mobile technologies for reflective on-the-job learning.

Existing Research

(From a thesis in School of Education, University of Leeds)

Goal:• Examine how the Activity Theory can be utilised to develop

personalised mobile learning environments to support reflective on-the-job training at Fire and Rescue Services.

Goal and Research Questions

Research Questions:• Can we use Activity Theory to inform the

design of an intelligent agent that captures a user’s risk assessment experience?

• How can a holistic model of context be developed by exploiting an ontological model of generic risk assessment and semantic enhanced location information?

• Can we design context-adapted dialogue to capture a user’s risk assessment experience and promote reflection?

Dynamic Risk Assessment

Ontology

Intelligent Dialogue AgentUser Login

Service

Semantic Location Service

Game Analyzer

Dialogue Game

useuse

use

maintain

Map Properties Database

uses

convert

UK Map from Ordnance Survey

A Sample Map Area(GML file)

link p

lace

related conce

pts

select extr

act l

ocat

ion

an

d pl

acesselect record

PORML System

Interact via Internet

Position x,y(GPS)

Reflective Question

User Dialogue InteractionLog File

Web-Based User Interface

User Current Activity

User Database

Personalised On-the-job Reflective Mobile Learning (PORML) Framework

Fire Incident Site

Incident Commander

Mobile Internet Browser

Incident Commander

Reflection-on-Action

After Fire Activity Completed

Review Fire Risk Assessment Activity

Fire Incident Site

Incident Commander


Incident Commander



Review Fire Risk Assessment ActivityCapture

Semantic Location

Capturing Semantic Location

A Sample Map Area(Ordnance Survey, UK)

Incident Location

Interest Area

+ GML File

Semantic Map Data

Incident Location

Interest Area

+ GML File

Semantic Map Data

Map Properties

ID Place or Building’ s

Name

Place or Building’ s

Typeor Area (Approximately)

Place Group

Easting Northing

001 Six Bells Pub Public House 551,997.92 256,249.54 Building

002 Six Bells Car Park

Car Park 552,015 256,247 Non-Building

003 Garage1 Garage 552,028.22 256,243.33 Building

’ s ’Centre Coordinate of Object

001 Six Bells Pub Public House 551,997.92 256,249.54 Building

002 Six Bells Car Park

Car Park 552,015 256,247 Non-Building

003 Garage1 Garage 552,028.22 256,243.33 Building

Some Property Entry

Dynamic Risk Assessment (DRA Ontology)

Garage

Public House

Car Park

Link Property to Concept


Ontology


Service


Game Analyzer

Dialogue Game

useuse

use

maintain


uses

convert



link p

lace

related conce

pts

select extr

act l

ocat

ion

an

d pl

acesselect record

PORML System


Position x,y(GPS)

Reflective Question




User Database


Fire Incident Site

Incident Commander


Incident Commander




Domain Ontology (DRA Ontology)

for Fire Risk Assessment

Creating Domain Ontology for Fire Risk Assessment

Chimney Fire

Assess Hazardous Substances

Working in Roof Space Activity Level

ActionLevel

Obtain Information on Risks

Operation Level

DRA Ontology

Chimney Fire

Working in Roof Space

Crew Member

Assess Hazardous Substance

Obtain Information on Risks

haveRole

haveRole

Assess HazardousSubstance

Crew Member


Ontology


Service


Game Analyzer

Dialogue Game

useuse

use

maintain


uses

convert



link p

lace

related conce

pts

select extr

act l

ocat

ion

an

d pl

acesselect record

PORML System


Position x,y(GPS)

Reflective Question




User Database


Fire Incident Site

Incident Commander


Incident Commander




A Main Part of PORML

(Dialogue Interaction)

Using Dialogue Interaction to Capture User Experience (Activity)

Collect User and Location Information

User Current Experience

Express Utterance (Agent)

User Utterance

Update

PlanCommunicativeAct

NoNext Utterance

Check End Dialogue

Analyse

Yes

Record Dialogue Log File

User Dialogue

Interaction Log File

DRA Ontology

Reflective

Question

Template

Start to get date and time of incident, position (x,y), and incident place

Collect Context Information DG

Initial Actions DG

Explanation DG

Initial Control Measures DG

(for first situation)

Identify Risk Assessment DG(identify hazards)

Mode and System DG

Additional Control Measure DG

Explanation DG

Reflection DG

Reflection DG

Explanation DG

Explanation DG

Explanation DG

Feedback DG

Start Dialog Game (DG)

Situation Assessment DG(who was harm, risk rating)

Reflection DG

Reflection DG

Suggest Actions

Explanation DG

Explanation DG

Feedback DG

Feedback DG

Reflection DG

Nex

t Situ

atio

n


No, I didn’t perform

Yes, I perform

Reflection1

Reflection2

No, I didn’t provide

Yes, I privide

Reflection1

Reflection2

Episode1

Episode2

Episode3

Episode4

Episode5

Episode6

Sub-Episode4

Sub

Sub

Sub

Sub

Sub-Episode

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Dialogue Structure

Explain concept (term meaning) in a statement

Explain concept

Explain concept

Explain concept

No, I didn’t identify

Explain concept

Explain concept

Feedback Selection/Result


PerformActions DG

End Dialog DG

Reflect Low RiskReflect Medium RiskReflect High Risk

As Fire Developed

Start to get date and time of incident, position (x,y), and incident place

Collect Context Information DG

Initial Actions DG

Explanation DG

Initial Control Measures DG

(for first situation)

Explanation DG

Reflection DG

Reflection DG

Explanation DG

Start Dialog Game (DG)

No, I didn’t perform

Yes, I perform

Reflection1

Reflection2

No, I didn’t provide

Yes, I privide

Reflection1

Reflection2

Episode1

Episode2

Episode3

Sub

Sub

Sub

Sub

Sub-Episode

Sub

Dialogue Structure

Explain concept (term meaning) in a statement

Explain concept

Explain concept

Identify Risk Assessment DG(identify hazards)

Mode and System DG

Additional Control Measure DG

Explanation DG

Explanation DG

Feedback DGSituation Assessment DG

(who was harm, risk rating)

Reflection DG

Reflection DG

Suggest Actions

Explanation DG

Explanation DG

Feedback DG

Feedback DG

Reflection DG

Nex

t Situ

atio

n


Episode4

Episode5

Episode6

Sub-Episode4

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

Explain concept

No, I didn’t identify

Explain concept

Explain concept



PerformActions DG

End Dialog DG

Reflect Low RiskReflect Medium Risk

Reflect High Risk

As Fire Developed

PORML Dialogue Example

Collect Basic Information

Dialog: AGENT What was the incident place name you deal with?

Dialog: User It was a SixBells Pub

SixBells Pub

Confirm/Send SixBells Car Park

Garage1

Building11

Building11a

BDa

Collect Initial User and Location InformationCollect Basic Information

Dialog: AGENT How were the weather conditions during fighting the Chimney Fire?

Dialog: User Weather Rain , Wind Low , Visibility Good

Freeze High Bad

Confirm/Send Rain Low Good

Snow

Sunny


Dialog: AGENT Which fire type do you want to assess?

Dialog: User I want to assess Chimney Fire

Building Fire

Confirm/Send Chimney Fire

Farm Fire

High Rise Building Fire

Public Entertainment Venue Fire

Rural Area Fire

Secure Accommodation Fire


Dialog: AGENT What was the incident place name you deal with?

Dialog: User It was a SixBells Pub

SixBells Pub

Confirm/Send SixBells Car Park

Garage1

Building11

Building11a

BDa

Example on iPhone 3G Used in Summative Evaluation

• A new context modelling algorithms that capture a user’s risk assessment experience based on semantic-enhanced geographic data and an ontological model of general risk assessment activity

• Demonstration how these algorithms can be utilised in a novel pedagogical environment to promote reflective learning

• Examination of whether the new technological solutions could be deployed in the Fire and Rescue Services training practice

Contribution

• This work presents a new opportunity for personalised reflective learning using dialogue to reflect the fire risk assessment activity in emergency services based on the user’s real job experiences

• The potential benefits of modelling this assessment context are quick risk assessment linked to the real situation

• The user experiences and location environments are involved and taken into account the risk assessment activities

Conclusions

Thank you!

Documents

Personalised Support for Reflective Learning in Fire Risk Assessment Wichai Eamsinvattana Supervisors: Vania Dimitrova, School of Computing David Allen,