Transcript Personalized and adaptive eLearning – approaches and solutions

Personalized and adaptive eLearning –
approaches and solutions
Radoslav Pavlov, Desislava Paneva
Institute of Mathematics and Informatics - BAS
[email protected]; [email protected]
Presentation overview
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Main issues of personalized and adaptive eLearning
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Learning customization and web services approach
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Development and design of adaptive learning content
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Student modelling
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Tailoring learning materials to the individual learning styles
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Personalization and learning content adaptation in learning
GRIDS
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References
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:
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how to find and filter the learning materials that fit the user
preferences, needs, background, learning style, etc.;
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how to present them;
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how to customize the learning process i.e. deliver just the
right material to the learner on Demand and Just in Time;
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how to give user tools to reconfigure the system;
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how to construct effective user model and tracking of its
continuous changes, etc.
Main issues of personalized and adaptive
learning
Types of personalization:
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Personalization of the learning context, based on
the learner’s preferences, background, experience, learning
style, etc.
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Personalization of the presentation manner and
form of the leaning content (for example, adaptive learning
sequences of learning objects);
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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.
Main issues of personalized and
adaptive learning
Personalization in current LMSs includes:
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Editable user profile;
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Changeable graphics design of the learning material;
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Personal calendar tracking learning progress events;
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Access to learning objects conditioned on part of the personal data
including achievements, experience, preferences, etc. (rarely);
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Information about the learner behaviour during the learning process
and the system’s reactions – personalized instructional flows,
adaptive learning content, etc. (rarely);
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Presentation manner and form of the learning content according to
learner’s style (rarely), etc.
Learning customization and web
services approach
Wlliam Blackmon and Daniel Rehak define the following ways for
learning customization:
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At random – repeat random selection of learning objects;
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By profile – choose the course/content based on the learner’s
profile (role, skills, learning style, etc.);
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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.;
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By response – choose the next learning activity based on the
learner’s responses to questions.
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.
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:
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User agents - provide interface between users and the learning services and
major element of LMS – authoring of content, management of learning, content
delivery, etc.;
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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.)
Learning customization and web
services approach
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.
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.
Student modelling
The student model enables the system to:
• provide individualized course content and study guidance;
• suggest optimal learning objectives;
• determine students’ profiles and their actual knowledge;
• dynamically assemble courses based on individual training
needs and learning styles;
• join a teacher for guidance, help and motivation, etc.
Student modelling - standards
Incorporation between IEEE LTSC’s Personal and Private Information
(PAPI) Standard and the IMS Learner Information Package (LIP)
Student modelling
SeLeNe learner profile
The Self e-Learning Networks Project (SeLeNe) is a one-year Accompanying Measure funded by
EU FP5, running from 1st November 2002 to 31st October 2003, extended until 31st January
2004
Tailoring learning materials to the
individual learning styles
Filtering
Keyword-based
search of LOs
Learner
of the
Ranking
result LOs
Presentation
User profile (individual
learning style)
Personalized learner’s view of the LO information space
Personalized LO
browsing process
according to:
Learner’s preferences help to the system to recommend
individualized LOs or categories of LOs.
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.
Personalization and learning content
adaptation in learning GRIDS
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ELeGI (European Learning Grid Infrastructure) is an EU-funded
Integrated Project that aims at facilitating the emergence of an
European GRID infrastructure for 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.
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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.
References
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Pavlov R., Dochev D. (2004), New Information Technologies and Interactive
Environments for Vocational and Life-long Learning, Analytical study, ICT
Development Agency, Sofia.
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Blackmon W. H., Rehak D. R, (2003) Customized Learning: A Web Services
Approach, Proceedings of the EdMedia 2003 conference, Honolulu, Hawaii, USA.
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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.
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Zheleva M. (2005), Design and development of Intended Instructional Flows in Webbased 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.
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Graziano A., Russo S., Vecchio V. (2003), Metadata-based Distributed Architecture
for Personalized Information Access, Proceedings of the European Distance and ELearning Network /EDEN/ Annual Conference “Integrating Quality Cultures in
Flexible, Distance and eLearning”, Rhodes, Greece, pp. 66-72.
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Keenoy K., Levene M., Peterson D. (2004), Personalisation and Trails in Self eLearning 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
References
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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
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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
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IMS Simple Sequencing Information and Behavior Model (2003), Version 1.0
Final.
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Sharable Content Object Reference Model, Available online:
http://www.adlnet.org.
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SeLeNe (Self eLearning Networks) - http://www.dsc.bbk.ac.uk/selene/
Project DILIGENT (Digital Library Infrastructure on Grid Enable Technology) http://www.diligentproject.org/
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ELeGI (European Learning Grid Infrastructure) project - http://www.elegi.org/