Personalized and adaptive eLearningPersonalized and adaptive eLearningApplications in LSMsApplications 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

3

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

4

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

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

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

SeLeNe learner profile

Adaptive learning system architecture

Adaptive content agent

Learning style monitoring agent

Login service

TutorAdaptive delivery

service

Learning style testing service

Advice agent

Content management service Learning content

database

Personal agent of tutor

Learners withdifferent learning styles

User profile database

Chat/Analyse

Chat/ Analyse

Inter-agent communication

Personal agents of learners

Other services

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

Preparing input data

Data-driven approach Literature-based approachOR

Predicted learning style preferences

LMS database

Automatic student modelling approaches

data-driven vs. literature-based

Felder-Silverman learning style model

Index of Learning Style questionnaire

Literature-based approach

Data-driven 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

Keyword-based search of LOs

Learner

Filtering

Ranking

Presentation

result LOs

of the

User profile (individual learning style)

Personalized learner’s view of the LO information space

Learner’s preferences help to the system to recommend individualized LOs or categories of LOs.

Personalized LO browsing process

according to:

Thanks for your attention!