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Ontology-Based Techniques for Context-Aware Personalization of

Educational Programs

Amir Bahmani1, Dr. Sahra Sedigh2, and Dr. Ali Hurson1

1Department of Computer Science2Department of Electrical and Computer Engineering

Sixth Annual ISC Graduate Research SymposiumApril 13, 2012

Outline• PERCEPOLIS

– Shortcomings of STEM Education– Modularity

• Context-Aware Systems• The Proposed Context-Aware System• Personalization Processes• Prototype• Conclusions

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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– Prevalent pedagogy is not well-suited to learning style of millennial

students.– Learning technologies are not used effectively.

• Lack of resources: skilled faculty, facilities, equipment

Consequences

Low enrollment, retention, and graduation rates in STEM programs. Students who do graduate are not prepared for “professional practice.”

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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

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Our Proposal

• The modular approach increases the resolution of the curriculum and allows for finer-grained personalization of learning objects and associated data collection.

Modularity

CS 388- High Performance Computer Architecture

Performance Metrics

RISC vs. CISC

Arithmetic Logic Unit

Beyond RISC

. . .

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CS - Curriculum

CpE 111 CS 388 CS XXX

ProgrammingCISC vs. RISC ALU Memory Beyond RISCPerformance Metrics

VHDLProgrammingCISC

Parallel and

Serial ALU

Content Accessible Memories

Performance Metrics

Address Accessible Memories

Concurrency

RISC VLIWFunctional

ALU Parallelism Pipelining

PrerequisiteRelation Modules

Superscalar for Beginner Study

Student Profile

…

Access Environment

Superscalar for Intermediate Study

Superscalar for Advance Study

Personalization Hierarchy

Superscalar

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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.

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Context-Aware Systems (cont’d)

• An ontology is a representation of the universe; it shows how different entities are related.

• Ontology-based modeling allows:1. knowledge sharing

2. logic inference

3. knowledge reuse

Cat

Lion Tiger

is-a is-a

Taxonomy

Cat

JungleCarnivoreTail

is-a lives inhas-a

Ontology

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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

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.

RecommenderSystem (RS)

Store / Retrieve

Context

PERCEPOLIS System Terms

Input Data

ContextDatabase

Context AttributesInferred Context

Context State Context Manager (CM)

Recommendation Context

Context Delivery

Context ManagementLayer

Context InterpreterLayer

Context ProviderLayer

Inference Engine (IE)

Generic OntologyDomain Ontology

RecommendationRequests & Feedbacks

Software Agent

Summary Schema Model

Context Verifier

Adaptive Presentation

Operation

RecommendationAlgorithms

Proposed Context-Aware System(cont’d)

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Personalization ProcessesCurriculum

Course

Topic

Subtopic

Module

Retrieve departmental rules

Find potential courses based on student’s profileand department rules

Find the most appropriate modules for The selected subtopics based on student’s profile (Student's infrastructure and background)

Personalization Processes PERCEPOLIS Student

Overall check on the selected courses

Prioritize the list based on Student’s interests and the result of collaborative filtering

Select desired courses

For each selected course

Retrieve tentative list of topics

Remove Topics have been taken

For each selected topic

Retrieve tentative list of topics

Remove subtopics have been taken or are being takenPrioritize the list based on Student’s interests and

the result of collaborative filtering

Select desired subtopics

Check whether the list satisfies the course constructor’s expectations. If “No”. Revise the list and add advanced topics

For all selected subtopic

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Prototyping

• The first version of the cyberinfrastructure prototype, based on the proposed context-aware system, is partially operational.

• The prototype and profile databases have been implemented in Java SE 6 and MySQL 5.5.8, respectively.

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Prototyping (cont’d)

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.

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Questions?

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