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(Personalization of learning material in web-based education)

Personalisering av læringsinnhold i e-læringskurs. (Personalization of learning material in web-based education). Håvard Narvesen 05HMTMT. Overview. Employer: Apropos Internett (Hamar, Norway) Main task: Study ways to adapt learning material based on individual competence gaps

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(Personalization of learning material in web-based education)

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  1. Personalisering av læringsinnhold i e-læringskurs (Personalization of learning material in web-based education) Håvard Narvesen 05HMTMT

  2. 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

  3. Introduction What 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?

  4. Problem area «One-size-fits-all»-scenario Personalized material

  5. 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

  6. 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?»

  7. Method • 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. • 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.

  8. 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.

  9. 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»

  10. The experiment • A test group of 11 learners used the prototype to carry out a web-based course. • The course concerned computer viruses. • A simple pre-test determined the available learning material.

  11. The structure of the course: • The pre-test was organized as follows: • 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.

  12. 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.

  13. 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.

  14. Thank you for your attention! Any comments or questions?

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