The 5R Adaptation Framework for LocationBased Mobile Learning Systems
Kinshuk, PhD
Associate Dean, Faculty of Science & Technology
Professor, School of Computing and Information Systems
NSERC/iCORE/Xerox/Markin Industrial Research Chair for Adaptivity and
Personalization in Informatics
Athabasca University, Canada
kinshuk@ieee.org
http://kinshuk.athabascau.ca
(jointly with Qing Tan, Xiaokun Zhang and Rory McGreal)
Slide 1
Overall research direction
• Individualised learning in increasingly global
educational environment
• Bridging the gap among different types of
learners
• Support for:
• Mobile and life-long learners
• Just-in-time and on-demand learning
• Context adaptation
Slide 2
Vision
~ Learning omnipresent and highly contextual ~
So how do we do it?
Slide 3
Adaptivity in ubiquitous learning
Extensive modelling of learner’s actions,
interactions, “mood”, trends of
preferences, skill & knowledge levels,
implicit and explicit changes in skill &
knowledge levels
Real-time monitoring of learner’s location,
technology use, and change of situational
aspects
Slide 4
Learner awareness
Personalization of learning experience through the
dynamic learner modeling
• Performance based model
• Cognitive trait model
• Learning styles
Slide 5
Dynamic learner modeling
Mining of historical and real
time data for real-time
adaptivity
•
•
•
•
•
•
•
•
Learning activities
Learning style
Interests & knowledge
Problem solving activities
Learning object/activity usage
Social activities
Learner location
Location related activities
Slide 6
Technology awareness
Personalization of learning experience through the
identification of technological functionality
• Identifying various device functionality
• Dynamically optimize the content to suit the
functionality
Display capability, Audio and video capability, Multilanguage capability, Memory, Bandwidth,
Operation platform
Slide 7
Location awareness
Personalization of learning experience through the
use of location modeling
•
Location based optimal grouping
•
Location based adaptation of learning
content
•
Location based collaborative creation of
authentic content
Slide 8
Location based technologies
Slide 9
Location aware dynamic grouping
T = t3
Location Grouping
F
C
G
D
A
E
T = t1
T = t2
B
Mobile Learner’s Address
Mobile Learner’s Cellular Data
Mobile Learner’s GPS Coordinates
Mobile Learner’s Other Location Info
t1 != t2!=t3
Mobile Virtual Campus
Mobile Learner’s Learning Profile
Mobile Learner’s Learning Style
Mobile Learner’s Learning Interests
Slide 10
Location based content creation
Slide 11
Real-life physical objects
Personalization of learning
surrounding environment
experience
as
per
Public databases of POIs
QR Codes
Wi-Fi & Bluetooth Access Point identification
Active and Passive RFIDs
Slide 12
Slide 13
Surrounding context
Personalization of learning experience through the
use of surrounding context
• Identifying specific context-aware knowledge
structure among different domains
• Identify the learning objective(s) that the learner
is really interested in
• Propose learning activities to the learner
• Lead the learner
environment
around
the
learning
Slide 14
5R Adaptation Framework
Slide 15
Introduction of framework
• A
conceptual
framework
for
the
implementation of Adaptive Mobile Learning
systems.
• An ontology model of the framework in which
the factors of Learner, Location, Time, and
Mobile Device are considered in generating
Personalized Learning Contents
Slide 16
Introduction of framework (cont. 1)
 Challenge
of facilitating mobile learning and
ensuring learners’ performance:
Presenting or generating
personalized learning contents
and instructions dynamically
Learning environment and
mobile device
The context of learning
process and instruction
Appropriately
identifying characters
of particular learner.
Dynamic Contents
Mobile Device
Context Aware
Learner Identification
Slide 17
Introduction of framework (cont. 2)
The challenge facing the development of locationbased adaptive learning applications is the ability
to deal with these contexts from Learning
Perspective.
One of the key strategies is to:
•
•
identify and normalize context information based on
efficient context-aware data fusion.
semantic-based context constraints using composable
ontology models.
Slide 18
Introduction of framework (cont. 3)
The Ontology-based approach:
•
Uses predefined metadata models of the learning
contents, learner models, context information of the
learning activities, and mobile device, etc.
•
Retrieve structured and unstructured learning
materials and generate personalized, just-in-time, and
location-aware learning contents or adaptive “filter”
that directs mobile learner to access right contents.
Slide 19
Introduction of framework (cont. 4)
The First approach:
• To create semantic learning contents manually.
The Second approach:
• To take advantage of pre-existing learning objects.
• To develop shareable ontologies, publishing learning
objects standard, and reward mobile service system to
make the learning objects widely accessible.
Our Research Aim:
To conduct bottom-up development of the ontology
for the personalized learning objectives,
learning context information and proposed
5R constraint information.
Slide 20
Introduction of framework (cont. 5)
The Third approach:
• To develop software and knowledge retrieval
mechanism that automatically identifies appropriate
learning components and extracts structural knowledge
from unstructured learning contents.
Our Research Aim:
To build and manipulate adaptive “filter”
to direct just-in-time retrieval paths
during the mobile learning processes.
• Learning contents are pre-developed and stored in the
learning contents repository of the learning
management system.
Slide 21
5R Adaptation Framework
Slide 22
5R Adaptation Framework
• The Right Time:
Factors: the Date-Time and the Learning Progress
Alberta Legislature:
Open: 09:00 AM
Close: 04:30 PM
Timing Match!
Show Contents!
Device Date Time:
03:25 PM
5R Adaptation Framework
• The Right Location:
The learner’s current geographic location
GPS Coordination Match!
Show Contents!
5R Adaptation Framework
• The Right Device:


HD Video Contents
Text Contents



Audio Contents
Web Page Contents
Flash Video
Contents
5R Adaptation Framework
• The Right Contents:
Learning objects, learning
activities, and leaning
instruction
LO A1
Back
Home
Design Manner of
Legislature Building
Take
Picture
Screen
LO
A1 Shot
Back
Home
Screen
Shot
Take
Picture
5R Adaptation Framework
• The Right Learner:
 Practical English
 Physical Education
 Computer Science
 Art
 Mathematics
Implementation of framework
The First layer:
• Consists of “Location”, “Time”, “Learner”, “Device”, and
“Learning Contents”, respectively representing the five
adaptation inputs.
The Second layer:
• Further description of information or data of each adaptation
input.
• The ontology scheme, namely, the relationships among the
adaptation inputs, which illustrates why the inputs need to be
described and how the inputs are interconnected.
Slide 28
Framework ontology schema
Slide 29
Framework application scenario: Concept
Slide 30
Framework application scenario: Field trip
Location-based mobile fieldtrip applications at a zoo
Slide 31
Framework application scenario: Field trip
Location-based mobile fieldtrip applications using visualized
interaction with dynamic geospatial data
Slide 32
5R adaptation features in the fieldtrip scenario
Location-based experiment Lab
interface and visualized interaction
on mobile devices.
Dynamic annotation or blog
on the visualized semantic physical object model.
Adaptive learning content retrieval
constrained by the location and
ongoingexperience
fieldtrip activities.
Real-time sharing
between students
and others who are in the field or in remote areas
via visualized
virtualfieldtrip
interaction
Visualized
plan,interface.
real-time activity collaboration and
monitoring during the fieltrip
Fieldtrip
Scenario
Slide 33
Framework application
scenario: RFID Classroom
System Screenshot: User Login
 System Screenshot:
Learning Object - Round Exicter @ Location code 80
Slide 34
Framework application
scenario: RFID Classroom
 System Screenshot:
Learning Object - Rectangular Exicter @ Location code 3F
Slide 35
Mobile and ubiquitous
educational environment
Slide 36
Slide 37
Thank you!
Slide 38