Diapozitiv 1
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Transcript Diapozitiv 1
METOD – MetaTool
for Educational
Platform Design
Mateja Verlič
University of Maribor
Faculty of Electrical Engineering and Computer Science
Contents
About project
METOD paradigm
Adaptive learning in METOD
Data collection
Supporting technologies
MetaTool
Meta-information
User
interface
About project
Leonardo da Vinci programme
Motivation:
Lack
of adaptiveness of current Learning
management systems
Static platforms
Ease of knowledge transfer
Lack of support for special groups of people
(elderly, vision and hearing impaired...)
METOD paradigm 1/2
Enabling educators to:
Educators:
Develop computerized educational platforms
Supporting individual educational and training approaches
Experiment with various platforms and innovative educational
methods with ease
Supporting and accelerating creativity
Teachers
Non-professional educators (parents, family members)
Self-adaptation
METOD paradigm 2/2
Long term aim: improve the skills and
competence of people by the integration of new
computer supported educational platforms
Paradigm is:
Collection
of theoretical and practical findings
Providing repository of:
various training environments
description of trainees' characteristics
possible pedagogical goals
different pedagogical and communication strategies
Adaptive learning in METOD 1/
Considering:
Various styles of learning – learning through:
Different student types and skill levels:
seeing,
listening,
moving,
doing and
touching
Basic
Intermediate
Advanced
Different learning paths
Adaptive learning in METOD
Preferred
learing styles
Learning theories
Learning and teaching methods, approaches,
processes
Life-span learning
Data collection
Direct feedback:
Surveys
Questionnaires
Polls
Grades
Indirect feedback
Observing
use of LMS resources
Observing use of tools
Supporting technologies
Analysis and evaluation of collected data
User classification and student types
Prediction of user’s behavior
Suggested learning path
Predefined paths
Effective path by other users
Suggested links of alternative learning styles and levels
Intelligent behavior
Data-mining
Machine learning - Multimetod approach
MetaTool
Meta-tool = Tool of tools
Output of MetaTool = input to LMS
Customizable multi-user type support
Input of MetaTool:
Topics
Materials
Meta-information
Collected
data from LMS
MetaTool meta-information
Why meta?
Information
about using materials
Not part of the material
User
types
Learning styles
Suggested learning path
Teacher defined
Machine generated
Possibility of peer review
MetaTool user interface
Basic user interface:
Parents
Other family members
Learners
Wizard-based creation of
courses
Advanced user interface:
Skilled professional
teachers
User interface
Defining topics
Assigning materials to topics
Output of MetaTool
SCORM package
Standard
for exchange of e-learning units between
LMSs
Compatibility support
Only static structure
Additional integration of meta-information
Enabling dynamic content
Add-on module for LMS needed
Static web pages
Moodle package
Add-on for LMS
Data collection module
Machine learning module
On-line machine learning
Off-line machine learning
Statistics
Minor adjustment of the learning path
Better analysis
More processing resources available
Can be peer reviewed
Inference module
Application of extracted knowledge
Advantages of METOD
Adaptiveness on different levels
Integration of learning theories and
intelligent systems
Accessibility
Support
for people with special needs
Support for life-long learning
MetaTool - not limited to single LMS
Disadvantages of METOD
Adaptiveness
User
does not get the same learning path
every time
Can be confusing for adult users
Can be overridden (predefined static view of the
content)
LMS module needed for intelligent support
Additional effort for teacher
Current status of METOD
Currently in active development phase
First user-feedbacks
Expected deployment at the end of the
year
Not yet publicly available
Summary
Advantages > disadvanatges
Why?
Less
effort needed for learner
Alternative materials and learning paths
More interresting?
Improving
the quality of learning process
Next logical step in LMS development
Thank you!
Questions?