Personalized and adaptive eLearning – approaches and

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Transcript Personalized and adaptive eLearning – approaches and

Personalized and adaptive eLearning
Applications in LSMs
Phạm Quang Dũng
Dept. of Computer Science
Content
• 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
Problem
Every learner has individual characteristics: learning
preference, self-efficacy, knowledge, goal, experience,
interest, background, etc.
How to enhance learning process effectively?
Solution: personalized and adaptive learning
• Adaptive system tunes learning material & teaching
method to learner model
3
Learner model
Learner profile: contains personal information without
inferring or interpreting.
Learner model: description of learner’s properties
– has a higher level than profile, expresses abstract
overview of learner
Learner modelling
4
Learning objects
any digital resource that can be reused to
support learning (D.A. Wiley, 2000)
– digital images or photos, video or audio snippets,
small bits of text, animations, a web page
Characteristics
– Share and reuse
– Digital
– Metadata-tagged
• Description information: title, author, format, content
description, instructional function
– Instructional and Target-Oriented
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.
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.
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.
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.
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:
-
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.)
Learning customization and web
services approach
Development and design of adaptive
learning content
Adaptive learning content 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 of 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
Adaptive learning system architecture
Personal agent
of tutor
Content management
service
Learning content
database
Tutor
Adaptive delivery
service
Adaptive content
agent
Chat/ Analyse
Learning style
monitoring agent
Advice agent
Login service
Learners with
different learning styles
Personal agents
of learners
Inter-agent
communication
Other services
Chat/Analyse
Learning style
testing service
User profile
database
Problems with collaborative student
modelling that use a questionnaire
Uncertainty because of:
– a lack of students’ motivation
– a lack of self-awareness about their learning
preferences
– the influence of expectations from others
Questionnaires are static and describe the
learning style of a student at a specific point of
time
– The result depends much on students’ mood
Benefits of using automatic
student modeling
does not require additional effort from students
is free of uncertainty
can be more fault-tolerant due to information
gathering over a longer period of time
can recognise and update the change of
students’ learning preferences
Automatic student modelling approaches
Determining relevant behaviour
Selecting features
and patterns
Classifying the
occurance of
behaviour
Defining patterns for
each dimentions
Inferring learning styles from behaviour
LMS
database
Preparing input data
Data-driven approach
OR
Literature-based approach
Predicted learning style preferences
Automatic student modelling approaches
data-driven vs. literature-based
Felder-Silverman
learning style model
Index of Learning
Style questionnaire
Data-driven
approach
Literature-based
approach
Automatic student modelling
The data-driven approach
uses sample data in order to build a model for
identifying learning styles from the behaviour
of learners
aims at building a model that imitates the ILS
questionnaire
Advantage: the model can be very accurate due
to the use of real data
Disadvantage: the approach strictly depends on
the available data and is developed for
specific systems
Automatic student modeling
The literature-based approach
uses the behaviour of students in order to get
hints about their learning style preferences
then applies a rule-based method to calculate
LSs from the number of matching hints
Advantage: generic and applicable for data
gathered from any course
Disadvantage: might have problems in
estimating the importance of the different hints
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.
Thanks for your attention!