(Personalization of learning material in web-based education)

Håvard Narvesen 05HMTMT

Personalisering av læringsinnhold i e-læringskurs

Overview

• Employer: Apropos Internett (Hamar, Norway)

• Main task: Study ways to adapt learning material based on individual competence gaps

• Supervisor: Rune Hjelsvold

• Keywords: E-learning, personalization, adaptive hypermedia

IntroductionWhat is a Learning Management System (LMS)?

What is the problem with presentation of most web-based education material today?

How can personalization improve web-based education?

Forhåndskunnskap

Deltaker A Deltaker B

Forhåndskunnskap

Problem area

«One-size-fits-all»-scenario Personalized material

Pre-knowledge

Learning material

Match

LearnerPersonalized

learning material

Learning material

«One-size.fits-all»-material

Learners

Why personalize learning material?

• It makes web-based courses more relevant to each learner.

• By making e-learning courses adaptable to each learner’s pre-knowledge, learners may start the same course at different entry levels.

• «If the learning material doesn’t feel relevant, then the learner’s motivation weakens». – Audun Gjevre, Apropos Internett

Research questions

• S1: «Hvilke egenskaper bør et nettbasert læringssystem inneha for å støtte personalisering av læringsinnhold basert på hver kursdeltakers kompetansegap?»

• S2: «Hvilke er de største tekniske utfordringene ved implementasjon av et adaptivt e-læringskurs, der innhold tilpasses basert på kursdeltakerens forhåndskunnskaper?»

• S3: «Hvordan oppfatter kursdeltakerne automatisert pretesting?»

Method

• S2: A prototype of a system, capable of personalizing learning material, was build in order to bring out major technical difficulties.

• S3: An experiment was carried out to get feedback from a set of learners on implemented personalization techniques. Qualitative and quantitative methods were used to gather data.

• S1: A literature study and an interview with an expert was used to understand relevant concepts and point out key characteristics of educational adaptive learning systems.

Some results – Study of characteristics (S1)

• By pre-testing each users knowledge prior to the web-based course, it is possible to unveil human competence gaps, and let them influence the personalization.

• The pre-test cannot be too resource-demanding neither for teachers or learners.

• Computer agents are commonly used to support learners in modern web-based educational systems.

Forhåndskunnskap

Læringsplan

Læringsmål

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ap

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Some results – Technical challenges (S2)

• Describing and dividing learning material suited for personalization. The SCORM standard is not perfectly suited for advanced personalization. (Abdullah et al., 2003)

• Building automated pre-tests, and then evaluate the results

• Automatically adapt learning material to each learner based on results from the pre-test and the learning goals. (Knowledge based)

• Implementation of agents for supporting adaptation «one learner – many teachers»

The experiment• A test group of 11 learners used the prototype to carry out a

web-based course.• The course concerned computer viruses.

Deltakere

Observasjon

PretestTilpasset

læringsinnholdPosttest

• A simple pre-test determined the available learning material.

• The pre-test was organized as follows:

Kursmodul 1

Kursmodul N-1

Kursmodul N

Kursmodul 2

...

Pretest

Forhåndskunnskap

• This means that the pre-test consists of the users pre-knowledge for each of the main topics in the course. The pre-knowledge was included as a part of a user model.

Course Course_topicLearning

objecthas has

(1,N) (1,N) (1,1)(1,1)

• The structure of the course:

Some results – Experiment (S3)• All participants agreed to spend 5% or more of

the total time a course demands in order to personalize a course (in the future).

• Only 2 of the 11 learners fully agreed with the technique for filtering learning material implemented in the prototype. These results confirms conclusions from other researchers that creating a system that can predict every learners competence gap with 100 % accuracy, is unrealistic.

• Also, the learners view on: Personalization in e-learning, how they like to be tested, how they liked link-personalization and more.

General conclusion (preliminary)

• The experiment in this work, and other studies, suggest that a pre-test should be used to decide which learners that need (or not need) extra attention, rather than entirely delimit the course material.

• Most test-learners did not like that the system totally decided what they should read and not. Based on information from the learners, the pre-test results should rather be used to make a suggestion of what to prioritize in the e-learning course.

Thank you for your attention!

Any comments or questions?