Ontology-Based Techniques for Context-Aware Personalization of Educational Programs

1. 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

2. Outline • PERCEPOLIS • Shortcomings of STEM Education • Modularity • Context-Aware Systems • The Proposed Context-Aware System • Personalization Processes • Prototype • Conclusions

3. 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.”

4. 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

5. Our Proposal

6. 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 . . .

7. Personalization Hierarchy CS - Curriculum … CS XXX CpE 111 CS 388 Beyond RISC Performance Metrics CISC vs. RISC ALU Memory Concurrency Programming Parallel and Serial ALU Address Accessible Memories Content Accessible Memories Functional ALU Performance Metrics VHDL Programming Superscalar VLIW Pipelining RISC CISC Parallelism Superscalar for Beginner Study Superscalar for Intermediate Study Superscalar for Advance Study Prerequisite Relation Student Profile Modules Access Environment

8. 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.

9. Context-Aware Systems (cont’d) • An ontology is a representation of the universe; it shows how different entities are related. • Ontology-based modeling allows: • knowledge sharing • logic inference • knowledge reuse Cat Cat is-a is-a lives in has-a is-a Lion Tiger Tail Carnivore Jungle Taxonomy Ontology

10. 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

11. 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.

12. 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.

13. Proposed Context-Aware System(cont’d) Inference Engine (IE) Inferred Context Context Attributes Context Management Layer Domain Ontology Generic Ontology Recommender System (RS) Context Database Store / Retrieve Context Context State • Context • Manager (CM) Recommendation Context Recommendation Algorithms Adaptive Presentation Operation PERCEPOLIS System Terms Context Interpreter Layer Summary Schema Model Context Verifier Input Data Context Provider Layer Recommendation Requests & Feedbacks Context Delivery Software Agent

14. Personalization Processes Personalization Processes PERCEPOLIS Student Curriculum 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 Course Select desired courses Overall check on the selected courses For each selected course Retrieve tentative list of topics Remove Topics have been taken Topic 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 Subtopic Prioritize the list based on Student’s interests and the result of collaborative filtering Select desired subtopics For all selected subtopic Find the most appropriate modules for The selected subtopics based on student’s profile (Student's infrastructure and background) Module

15. 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.

16. Prototyping (cont’d)

17. 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.

18. Questions?