Ontology-Based Techniques for
Context-Aware Personalization of
Educational Programs
Amir Bahmani1, Dr. Sahra Sedigh2, and Dr. Ali Hurson1
1Department
of Computer Science
2Department of Electrical and Computer Engineering
Sixth Annual ISC Graduate Research Symposium
April 13, 2012
1
Outline
• PERCEPOLIS
– Shortcomings of STEM Education
– Modularity
•
•
•
•
•
Context-Aware Systems
The Proposed Context-Aware System
Personalization Processes
Prototype
Conclusions
2
Current Shortcomings of STEM Education
• Static and linear curricula
– Inability to keep up with advances in technology
– Redundancy AND lack of reinforcement of topics among courses
• Static and linear teaching practices
Consequences
– Prevalent pedagogy is not well-suited to learning style of millennial
students.
Low enrollment,
retention,
andused
graduation
rates in STEM programs.
– Learning
technologies
are not
effectively.
• LackStudents
of resources:
faculty,
facilities,
who doskilled
graduate
are not
preparedequipment
for “professional practice.”
3
Solutions Proposed
• By National Academy of Engineering:
– Personalized learning – identified as one of 14 Grand Challenges
in Engineering for the next century
• By President Obama’s Strategy for American Innovation:
– Use of learning technologies in higher education – listed as one of
six educational objectives
• Common sense (and overwhelming evidence)
– Resource sharing
– Teaching collaboration
– Active and peer learning
4
Our Proposal
5
Modularity
• The modular approach increases the resolution of the
curriculum and allows for finer-grained
personalization of learning objects and associated
data collection.
CS 388- High Performance Computer Architecture
Performance
Metrics
RISC vs.
CISC
Arithmetic
Logic Unit
...
Beyond
RISC
6
Personalization Hierarchy
CS - Curriculum
CpE 111
Performance
Metrics
Performance
Metrics
RISC
CISC vs. RISC
CISC
Parallel
and
Serial
ALU
ALU
Memory
Beyond RISC
Content
Functional Address
Accessible Accessible Superscalar
ALU
Memories Memories
Superscalar for
Beginner Study
Prerequisite
Relation
…
CS 388
Student
Profile
Superscalar for
Intermediate Study
Access
Environment
CS XXX
Concurrency
Programming
VLIW Parallelism Pipelining
VHDL
Programming
Superscalar for
Advance Study
Modules
7
Context-Aware Systems
• Context-awareness:
– The use of context in software applications that
adapt their behavior based on the discovered
context.
• Any context-aware system contains two main
parts:
– 1) Context management subsystem concerned with context
acquisition and dissemination
– 2) Context modeling concerned with recognizing,
representing, and manipulating context and situations.
8
Context-Aware Systems (cont’d)
• An ontology is a representation of the universe; it
shows how different entities are related.
Cat
Cat
is-a
is-a
Lion
Tiger
Taxonomy
has-a
Tail
is-a
lives in
Carnivore
Jungle
Ontology
• Ontology-based modeling allows:
1. knowledge sharing
2. logic inference
3. knowledge reuse
9
Proposed Context-Aware System
• The strengths of our system are:
– Leveraging both individual and peer group
information to offer better recommendations
– Being flexible and user-friendly
– Exceeding the functionality of competing
alternatives
– Updating the content of recommendations based
on student’s environment
10
Related Literature
• The C-CAST context management architecture
supports mobile context-based services by
decoupling provisioning, and consumption.
– The system is built based on three basic functional entities:
the context consumer (CxC), context broker (CxB), and
context provider (CxP)
• Hybrid Context Management (HCoM) uses semantic
ontology and relational schema to represent
graphical context data.
Related Literature (cont’d)
• A context aware framework (CAF) enables the
context-aware applications and services, while being
domain-agnostic and adaptable.
– The CAF contains two core components: the data acquisition
component and the context manager.
Proposed Context-Aware System(cont’d)
Inference Engine (IE)
Context
Management
Layer
Domain Ontology
Inferred Context
Context
Interpreter
Layer
Context
Provider
Layer
Recommender
System (RS)
Context State
Recommendation
Algorithms
Recommendation
Context
Adaptive
Presentation
Operation
Generic Ontology
Context Attributes
Context
Manager (CM)
Store / Retrieve
Context
Context
Database
PERCEPOLIS System Terms
Context Verifier
Recommendation
Requests & Feedbacks
Summary
Schema Model
Input Data
Context Delivery
13
Software Agent
Personalization Processes
Personalization
Processes
Curriculum
Course
PERCEPOLIS
Student
Retrieve departmental rules
Find potential courses based on student’s profile
and department rules
Prioritize the list based on Student’s interests and the
result of collaborative filtering
Select desired courses
Overall check on the selected courses
For each selected course
Topic
Subtopic
Retrieve tentative list of topics
Remove Topics have been taken
Check whether the list satisfies the course constructor’s
expectations. If “No”. Revise the list and add advanced
topics
For each selected topic
Retrieve tentative list of topics
Remove subtopics have been taken or
are being taken
Prioritize the list based on Student’s interests and
the result of collaborative filtering
Select desired subtopics
For all selected subtopic
Module
Find the most appropriate modules for The
selected subtopics based on student’s profile
(Student's infrastructure and background)
14
Prototyping
• The first version of the cyberinfrastructure
prototype, based on the proposed contextaware system, is partially operational.
• The prototype and profile databases have
been implemented in Java SE 6 and MySQL
5.5.8, respectively.
15
Prototyping (cont’d)
16
Conclusion
• In this work within the scope of PERCEPOLIS:
– A new layered context-aware system is presented
– The functionalities and strengths of the proposed
system are verified by the help of the first
prototype of the system
• Future work includes enhancing and performing
predictive modeling of the recommendation
algorithms for performance and accuracy.
17
Questions?
18