Personalized and adaptive eLearning – approaches and solutions

1. Personalized and adaptive eLearning – approaches and solutions Radoslav Pavlov, Desislava Paneva Institute of Mathematics and Informatics - BAS radko@cc.bas.bg; dessi@cc.bas.bg

2. Presentation overview • Main issues of personalized and adaptive eLearning • Learning customization and web services approach • Development and design of adaptive learning content • Student modelling • Tailoring learning materials to the individual learning styles • Personalization and learning content adaptation in learning GRIDS • References

3. Main issues of personalized and adaptive learning • The personalization is a function able to adapt the eLearning content and services to the user profile. It include: • - how to find and filter the learning materials that fit the user preferences, needs, background, learning style, etc.; • - how to present them; • - how to customize the learning process i.e. deliver just the right material to the learner on Demand and Just in Time; • - how to give user tools to reconfigure the system; • - how to construct effective user model and tracking of its continuous changes, etc.

4. Main issues of personalized and adaptive learning • Types of personalization: • - Personalization of the learning context, based on the learner’s preferences, background, experience, learning style, etc. • - Personalization of the presentation manner and form of the leaning content (for example, adaptive learning sequences of learning objects); • - Full personalization, which is a combination of the previous two types. • Adaptive learning means the capability to modify the learning content and/or any individual student’s learning experience as a function of information obtained through its performance and progress on situated tasks or assessments.

5. Main issues of personalized and adaptive learning • Personalization in current LMSs includes: • Editable user profile; • Changeable graphics design of the learning material; • Personal calendar tracking learning progress events; • Access to learning objects conditioned on part of the personal data including achievements, experience, preferences, etc. (rarely); • Information about the learner behaviour during the learning process and the system’s reactions – personalized instructional flows, adaptive learning content, etc. (rarely); • Presentation manner and form of the learning content according to learner’s style (rarely), etc.

6. Learning customization and web services approach • Wlliam Blackmon and Daniel Rehak define the following ways for learning customization: • At random – repeat random selection of learning objects; • By profile – choose the course/content based on the learner’s profile (role, skills, learning style, etc.); • By discovery – for given learning objective, find a learning object that best meets the learning objective given the learning’s current skill set, learning platform, learning style, language preference, etc.; • By response – choose the next learning activity based on the learner’s responses to questions.

7. Learning customization and web services approach • Wlliam Blackmon and Daniel Rehak offer a web-services-based methodology for customization by profile in particular a methodology for eliminating learning objects (LOs) from the course because either: • - the learner’s current role does not require the learning objective taught by the LO, or • - the learner’s profile indicates that the learner has already achieved the objective taught by the LO. • The learning content and data used for customization are presented in a set of standards-based data models.

8. Learning customization and web services approach • The overall web-services architecture for learning is divided into layered services. The layers from top to bottom are: • - User agents - provide interface between users and the learning services and major element of LMS – authoring of content, management of learning, content delivery, etc.; • - Learning services – they are collection of data models and independent behaviours. They are grouped into logical collections • - tool layer – provide public interface to the learning tools (simulators, assessment engines, collaboration tools, registration tools, etc.) • - common application layer (sequencing, managing learner profiles, content management, competency management, etc.) • - basic services layer – core features and functionality that are not specific for the learning (storage, management, workflow, right management, query/data interfaces, etc.)

9. Learning customization and web services approach

10. Development and design of adaptive learning content • Adaptive learning content is can be defined as a relevant sequence of learning objects (LOs), each of them associated with learning activity that fulfill given learning objective. The flows of learning activities can be described by rules and actions that specify: • - the relative order in which LOs have to be presented, and • - the conditions under which a pieces of content have to be selected, delivered or skipped during sequence presentation • according to the outcomes of learner’s interactions with content.

11. Development and design of adaptive learning content • The process of defining a specific sequence of learning activities begins with the creation of a learning strategy for the achievement of the determined pedagogical aim/s. Learning strategy specifies: • - types learning activities; • - their logical organization; • - the prerequisites, and • - expected results for each activities. • IMS Simple Sequencing Specification and the SCORM standard allow the learning strategies to be translated into sequencing rules and actions based on learner progress and performance.

12. Student modelling • The student model enables the system to:

13. Student modelling - standards • Incorporation between IEEE LTSC’s Personal and Private Information (PAPI) Standard and the IMS Learner Information Package (LIP)

14. Student modelling • SeLeNe learner profile • The Self e-Learning NetworksProject (SeLeNe) is a one-year Accompanying Measure fundedby • EU FP5, running from 1st November 2002 to 31st October 2003, extended until 31stJanuary 2004

15. Tailoring learning materials to the individual learning styles Filtering of the Keyword-based search of LOs Ranking result LOs Learner Presentation User profile (individual learning style) Personalized learner’s view of the LO information space PersonalizedLO browsing process according to: Learner’s preferences help to the system to recommend individualized LOs or categories of LOs.

16. Personalization and learning content adaptation in learning GRIDS • GRID can provide infrastructure that would allow learning process actors to: • - collaborate; • - take part in realistic simulations; • - use and share personalized high quality learning data in contextualized and ubiquitous way; • - innovate solutions of learning and training; • - manage dynamic conversations, etc.

17. Personalization and learning content adaptation in learning GRIDS • ELeGI (European Learning Grid Infrastructure) is an EU-funded Integrated Project that aims at facilitating the emergence of an European GRID infrastructurefor eLearning and stimulating the research of technologies to enhance and promote effective human learning. The project is supported by the European Community under the IST programme of the 6th Framework Programme. • Project DILIGENT (Digital Library Infrastructure on Grid Enable Technology) is an integrated project funded by EC FP6 IST Programme. DILIGENT is aimed at the creation of virtual digital libraries on the basis of grid-based infrastructure so that the integration of metadata, personalization services, semantic annotation, and on-demand availability of information collection and extraction be supported.

18. References • Pavlov R., Dochev D. (2004), New Information Technologies and Interactive Environments for Vocational and Life-long Learning, Analytical study, ICT Development Agency, Sofia. • Blackmon W. H., Rehak D. R, (2003) Customized Learning: A Web Services Approach, Proceedings of the EdMedia 2003 conference, Honolulu, Hawaii, USA. • Paneva D. (2005), Some Approaches for Personalization in Learning Management Systems, In D. Dochev, R. Pavlov (Eds.) “e-Learning solutions – On the Way to Ubiquitous Applications”, Proceedings of the Joint KNOSOS-CHIRON Open Workshop, Sandanski, Bulgaria, pp. 65-74. • Zheleva M. (2005), Design and development of Intended Instructional Flows in Web-based Learning Environments, In: I. Simonics, R. Pavlov, T. Urbanova (Eds.) “Technology-enhanced Learning with Ubiquitous Applications of Integrated Web, Digital TV and Mobile Technologies”, Proceedings of the HUBISKA Open Workshop, 6th eLearning Forum, Budapest, Hungary, pp. 49-60. • Graziano A., Russo S., Vecchio V. (2003), Metadata-based Distributed Architecture for Personalized Information Access, Proceedings of the European Distance and E-Learning Network /EDEN/ Annual Conference “Integrating Quality Cultures in Flexible, Distance and eLearning”, Rhodes, Greece, pp. 66-72. • Keenoy K., Levene M., Peterson D. (2004), Personalisation and Trails in Self e-Learning Networks, project: SeLeNe – Self E-Learning Networks, Deliverable 4.2, Available Online: http://www.dcs.bbk.ac.uk/selene/reports/Del4.2-2.1.pdf

19. References • Smythe C., Tansey F., Robson R. (2001), IMS Learner Information Package Information Model Specification, Technical report, Available Online: http://www.imsglobal.org/profiles/lipinfo01.html • IEEE p1484.2/d7, 2000-11-28 Draft Standard for Learning Technology - Public and Private Information (PAPI) for Learners (PAPI Learner), Technical report, Available Online: www.edutool.com/papi/papi_learner_07_main.pdf • IMS Simple Sequencing Information and Behavior Model (2003), Version 1.0 Final. • Sharable Content Object Reference Model, Available online: http://www.adlnet.org. • SeLeNe (Self eLearning Networks) - http://www.dsc.bbk.ac.uk/selene/ • Project DILIGENT (Digital Library Infrastructure on Grid Enable Technology) - http://www.diligentproject.org/ • ELeGI (European Learning Grid Infrastructure) project - http://www.elegi.org/