Earticle

초록

영어

This paper is a part of ongoing research that tries to approve our hypothesis about adaptive personalization in web-based learning systems; we are debating that building online learning environments with the ability to detect learners learning style dynamically by observing their behaviour and then presenting learning material based on the detected learning style is more effective than using ILS questionnaire. In an earlier work, we provided a dynamic technique that identifies learners VAK learning styles according to their behaviour in the learning environment influenced by literature approach, in this paper we have modified our technique and re-proposed it. First, we connected behavioural patterns (time, visits and answer patterns) to the features (contents, outlines, group forums, examples, case studies, exercises and assessments), then we defined the effect each VAK learning style will have on each pattern. Next, we described three general rules and three algorithms that should detect the learning style. Now we are in the process of building two online learning environments to test and confirm the differences between dynamic learning style detection _ based on our technique _ and the traditional ILS questionnaire environments; we choose a course called ‘Computer Skills for medicine faculty students’, which is provided from the faculty of Information Technology at the University of Jordan; the results of the planed experiment will be saved and analysed using SPSS.

목차

Abstract
 1. Introduction
 2. Learning Style
  2.1. Learning Style Detection: Data Driven Approach or Literature Based Approach?
  2.2. Why VAK?
 3. Related Work
 4. Methodology
 5. Behaviour Patterns
 6. Rule Base
  6.1. Staying Time Pattern Rule
  6.2. Visits Pattern Rule
  6.3. Answers Pattern Rule
 7. Algorithms and Learning Style Estimation
  7.1. First Algorithm –Stay_Time_Factor
  7.2. Second Algorithm –Visits_Time_Factor
  7.3. Third Algorithm – Answers_Factor
 8. Hypothesis and Experiment Plan
 9. Conclusion
 References

키워드

저자

• Hadeel Ateia [ Department of Computer Information Systems, The University Of Jordan ]
• Thair Hamtini [ Department of Computer Information Systems, The University Of Jordan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605