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A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences

A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences

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A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences

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  1. A Grid Based Software Architecture for Delivery of Adaptive and Personalized Learning Experiences Authors: Angelo Gaeta , Matteo Gaeta and Pierluigi Ritrovato Presenter: Vinay Macherla

  2. Introduction • ELeGI project. • Radically advance the effective use of technology enhanced learning. • Knowledge construction in an individualized way using collaborative learning approaches, Leverage on Grid technologies. • Address issues relating to formal and informal learning.

  3. Creation and Delivery of UoL ELeGI approach consists of: • Definition of a general Learning Model. • Generate a Unit of Learning (UoL) and to dynamically adapt it during the learning process according to the learner’s behaviour. • Operational process and Theoretical learning model

  4. Operational Process to Build an Adaptive UoL Knowledge Building : formalization of knowledge related to domain. UoL Building : Learning objectives to be achieved, identifying target concepts and building a skeletal structure for the learning experience. UoL Delivery : Runtime execution of UoL, discovery of resources satisfying metadata specifications.

  5. Virtual Scientific Experiment Model • The VSE model fits learning aspects within a constructivistic vision: the role of the learner, the importance of context and collaboration. • VSE model macro phases: • Presentation • Practical situation • Abstract situation • Institutionalisation

  6. The VSE Model

  7. Contd. • Presentation Phase: Provides description of the didactic experience the student is about to start. • Practical Situation: Learner lives the experience. • Abstract Situation: Extrapolates abstract model representation • Institutionalization: Learner approved procedural and semantic correctness of concepts.

  8. ELeGI Architecture Application Layer E-Learning Application Contents & Services Orchestration Learning Services E-Learning Layer Course Management Services Didactical Model Mangm. Services Learning Metadata Services Support Services Knowledge Management Services Learner Profile Management Services Personalization Services Ontology Management Services Security Semantic Communication & Collaboration Services Discovery& Semantic Annotation Services Trust Services Negotiation Services VLC Services Billing Services VLC Management Services Member Profile Management Services Policy Services Grid Layer Execution Management Services Accounting Services Self Management Services Security Services Data Services Monitoring Services Resource Management Services Information Services Core Services Infrastructure Services

  9. Contd. • Environment Management Services: provides services and tools to support the creation, operation, and maintenance of a learning community. • Learning Services: provides services and tools to support the execution of the three processes of the Learning Model. • Application Layer: uses the services provided by the underlying layer to implement application in the e-learning domain.

  10. InfrastructureServices Access to Learning Object Repository Information Services Monitoring Services Accounting Services ResourceManagement Security Services Execution Management Self Management Grid technologies Grid It facilitates the realization of ubiquitous computing concept The Grid technologies are considered the natural evolution of distributed systems and the Internet It allows the virtualization and sharing of several kind of resources facilitating the dynamic context generation It facilitates the creation of emerging challenging learning scenarios through dynamic VO It provides services and advanced mechanisms for automatic discovery and binding of new suitable contents and services Enabling the creation of dynamic, distributed and heterogeneous Virtual Learning Communities

  11. Virtual Learning Communities (VLC) VLC Layer provides general and re-usable services for the lifecycle management of virtual communities. Discovery and Semantic Annotation Services • offer semantically-enabled registries and key features to publish service descriptions • support basic ontology management such as editing, browsing, mapping, consistency and validation, versioning; • capture annotation and dynamically link resources based on those annotations; • take advantage from the semantic enabled registries to enable more sophisticated discovery Communication/Collaboration Services • support synchronous and asynchronous interaction (email, forum, instant messaging, chat, …) • support different media formats (text, image, audio, video, and their combination) • support many communication models (one-to-one, one-to-many, broadcast, many-to-many) Billing Services • charge the use of services and resources • prepare and send bill VLC Management Services • provide administration utilities for the management of the Virtual Community • virtual community definition and creation • member registration/deregistration • … Trust Services • provide basic trust capabilities • support recommendation • support delegation Policy Services • allow the management of: • role of the community members • privilege of the community members • policy to access/use resources Member Profile Management Services • allow the management of the profile information of the Community Members • support information privacy Negotiation Services • allow negotiation of the agreement on the provision of a service • support Quality of Services

  12. Learning Services The e-Learning services facilitate and manage the learning process. Support Services • Alert Services • Help Services providing help features to assist learners in achieving their learning objectives • Assessment Services, providing online facility to check learning progress during and at the end of the course • e-Portfolio Services, supporting the management and assessment of artefacts created by learners • Reporting Services, providing facilities for producing standardized and automated reports on data • … Contents & Services Orchestration • searching and collecting dynamically contents and services • composition and orchestration of a didactical course (contents and services) • use the didactical and knowledge models • deliver contextualised learner services Course Management Services • access and manage courses, modules, and other units of learning • administration utilities (assignment management, student/staff management, assignment/submission evaluation, …) Learning Metadata Services • provide metadata services for learners and learning resources, including • Resource registration (i.e. providing metadata), • Metadata management, • Search and evaluation. Didactical Model Management Services • provide operation to manage the didactical models: • create, • edit, • validate, • browse, • … Learner Profile Management Services • allow the management of learner profile information: • Student Cognitive State • Learning Preferences • allows automatic update as a consequence of the new learning experiences performed Personalization Services • dynamically adapting and delivering of the learning resources • personalize the learning paths according to learner profile and needs (i.e. Adaptive Learning Path Generation Services that allow to automatically produce a personalized learning path for each learner) Ontology Management Services • extend the ontology services provided by the lower VLC sub-layer for learning domain.

  13. Knowledge and Didactic Models The general e-learning model allows the construction of context-based and personalised learning paths Extensibility and flexibility Implication of the student

  14. E-learningmodel Didactic Transposition • From the knowledge to the concrete knowledge • From the concrete knowledge to the contextualised didactic knowledge • From the contextualised didactic knowledge to the personalised didactic knowledge

  15. E-learning model Didactic Transposition • Definition of the Target of Learning • Definition of the sequencing of Elementary Metadata Concepts(ECM) • Definition of the Unit of Learning

  16. Context-based ontology The Generic Contextualised Ontology (GCO) will keep the same base structure of the meta-ontology but will bring with itself some metadata, derived from the Context, that will describe one or more families of concepts.

  17. IMS-LD: to define learning scenarios • Describe and implement learning activities based on different pedagogies, including group work and collaborative learning • Coordinate multiple learners and multiple roles within a multi-learner model, or, alternatively, support single learner activities • Coordinate the use of learning content with collaborative services • Support multiple delivery models, including mixed-mode learning • IMS Learning Design also enables: • Transfer of learning designs between systems • Reuse of learning designs and materials • Reuse of parts of a learning design, e.g. individual activities or roles • Internationalisation, accessibility, tracking, reporting, and performance analysis, through the use of properties for people, roles and learning designs

  18. Scenario Description:Physics course in the Open University Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University) Purpose: Target Group: HOU students students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge Main Characteristics: formal (but highly diverse student population) Type of learning: Type of services needed: Virtual Experiments/ Virtual Communities Support

  19. The context Physics Course: • 4-year course leading to a Bachelor Degree in Natural Sciences • 12 modules + 3 laboratory • (3 modules related to Physics: 7 text books suitable for Open and Distance Learning) • Student attendance: > 2500 students • Permanent Academic Staff (Prof., Ass. Prof.) • Tutors (Phd holders) • Students organized in classes based in specific cities Physics Lab DMSC Lab

  20. The context: City coverage Teaching method: • Text books • Synchronous & Asynchronous collaboration tools (…but mainly email/WWW is used) • Class meetings (a form of social learning) • Assignments (4-6 per module) Class/student distribution

  21. The context:User Needs • Knowledge construction : • Perform experiment (visualisation of data sets and output) • Search for resources and/or share results • Access supporting educational material • Perform on-line test/essay • Virtual Communities support (social learning): • Collaborate using asynchronous sharing services (e.g. sharing documents, knowledge, VSE results etc.) • Collaborate using synchronous sharing services during an experiment (with other students and/or the tutor)

  22. Scenario Setup • Legend • Super Node • Super Node • Nodes • Backbone : GUNet (155 Mbps)

  23. Data layer Resource “Z” Data layer Data layer Resource “Y” Course Personalization service Localization Service Web GUI (WSRP) Data layer Resource “X” Scenario Execution Invoke the Localization Service in order to find the list of Course Services

  24. Locator Service UDDI Data Layer (Learning Object Repository) Data Layer (Learning Object Repository) Data Layer Course Driver Instance The Course Driver Service contacts the Data Layer to retrieve the Student Model and Ontology and it invokes the Course Personalisation Service The Course Personalization Service, on the basis of the Student Model and the Ontology, generates the personalized learning path Obtained the Learning Path, the Course Driver is able to find and create an instance of a Driver service able to manage the resource of the Course The Client interacts with the Instantiator Service to create a new Course Driver Instance The Client interacts with the Localization Service to find a list of Course Services Course Personalization service Course Personalization service Request the delivery of the Course Find the list of the drivers which are able to delivery the Resource Requests the delivery of Resource “X” Scenario Execution Asks for a Personalized Learning Path Invoke the IS in order to create a Corse Driver Instance Personalized Learning Path Instantiates a suitable driver for Resource X Builds Web GUI for delivery of Resource X Retrieve LO Instantiates a suitable driver for Resource “Y” Builds Web GUI for delivery of Resource “Y” Retrieve LO

  25. The Grid added value to ELeGI (1) • Grid technologies: • Rely upon a dynamic and stateful service model and this affects also the development of learning scenarios • Provide dynamicity and adaptiveness to LD scenarios • Provide the scale of computational power and data storage needed to support realistic and experiential based learning approaches involving 3d simulations and Virtual Reality

  26. The Grid added value to ELeGI (2) • Grid technologies: • Are demonstrating their effectiveness for implementing e-Science infrastructure for sharing and manage data • Through the virtualization and sharing of several kind of resources facilitate the dynamic contexts generation • The dynamic service discovery and creation will allow the true personalisation

  27. Conclusions • Effective re-use of resources: exploiting the ELeGI software architecture it is possible to re-use all the building blocks of a UoL • Extensibility wrt services integration

  28. Questions