Multimedia Tools and Applications

, Volume 75, Issue 21, pp 13193–13209 | Cite as

UX based adaptive e-learning hypermedia system (U-AEHS): an integrative user model approach

Article

Abstract

Adaptive E-Learning Hypermedia System (AEHS) has been known as a method to provide the optimized learning for each learner’s unique characteristics. To this end, learning system developers configure the general characteristic elements of learners in AEHS and analyze learning functions customized for each feature. After then, they develop system to provide learning contents suitable for each learner through the analyzed data thereof. However, there are various learning functions in learning system. Moreover, each individual learner has a different set of characteristic elements. Therefore, it is very difficult to establish application criteria. In particular, it is imperative to have all profile data of learners in e-learning system in order to provide customized learning for each individual learner. Also, it is required to select and provide adequate learning contents by analyzing accurately necessary elements for learning. However, it is very difficult to analyze and determine what learning is necessary for learners and also which learning process is adequate for learners. In this regard, this study proposed Adaptive E-Learning Hypermedia System (AEHS) that leveraged UX (user experience) to provide optimal learning process customized for learners in e-learning system. The basic data model of UX leveraged user profile based on learning style. Learning style for learning has a large number of elements. Thus, this system defined those elements that could reflect learner characteristics from human factors. After then, this study profiled based on the actual data of learners for each characteristic. In this way, this system analyzed accurately which learning would be necessary for learners. In the end, this system proposed required learning contents for learners when learners selected learning based thereon.

Keywords

Multimedia based learning AEHS UX based learning User profile Learning style 

References

  1. 1.
    Ahmad N, Tasir Z, Kasim J, Sahat H (2013) Automatic detection of learning styles in learning management systems by using literature-based method. Proc Soc Behav Sci 103:181–189CrossRefGoogle Scholar
  2. 2.
    Akbulut Y, Cardak CS (2012) Adaptive educational hypermedia accommodating learning styles: a content analysis of publications from 2000 to 2011. Comput Educ 58:835–842CrossRefGoogle Scholar
  3. 3.
    Fagan M, Khan MMH, Buck R (2015) A study of users’ experiences and beliefs about software update messages. Comput Hum Behav 51(Part A):504–519CrossRefGoogle Scholar
  4. 4.
    Felder RM, Silvermann LK (1988) Learning and teaching styles in engineering education. J Eng Educ 78:674–681Google Scholar
  5. 5.
    Fraser J, Plewes S (2015) Applications of a UX maturity model to influencing hf best practices in technology centric companies—lessons from Edison. Proc Manuf 3:626–631Google Scholar
  6. 6.
    Gerjets P, Scheiter K, Opfermann M, Hesse FW, Eysink THS (2009) Learning with hypermedia: the influence of representational formats and different levels of learner control on performance and learning behavior. Comput Hum Behav 25:360–370CrossRefGoogle Scholar
  7. 7.
    Hwang G-J, Chiu L-Y, Chen C-H (2015) A contextual game-based learning approach to improving students’ inquiry-based learning performance in social studies courses. Comput Educ 81:13–25CrossRefGoogle Scholar
  8. 8.
    Jokinen JPP (2015) Emotional user experience: traits, events, and states. Int J Hum Comput Stud 76:67–77CrossRefGoogle Scholar
  9. 9.
    Kima MJ, Oh MW, Kim JT (2013) A method for evaluating the performance of green buildings with afocus on user experience. Energy Build 66:203–210CrossRefGoogle Scholar
  10. 10.
    Lasa G, Justel D, Retegi A (2015) Eyeface: a new multimethod tool to evaluate the perception of conceptual user experiences. Comput Hum Behav 52:359–363CrossRefGoogle Scholar
  11. 11.
    Law EL-C, Abrahão S (2014) Interplay between user experience (UX) evaluation and system development. Int J Hum Comput Stud 72(6):523–525CrossRefGoogle Scholar
  12. 12.
    Law EL-C, Schaik P, Roto V (2014) Attitudestowardsuserexperience(UX)measurement. Int J Hum Comput Stud 72:526–541CrossRefGoogle Scholar
  13. 13.
    Law EL-C, van Schaik P, Roto V (2014) Attitudes towards user experience (UX) measurement. Int J Hum Comput Stud 72(6):526–541CrossRefGoogle Scholar
  14. 14.
    Lee L-T, Hung JC (2015) Effects of blended e-learning: a case study in higher education tax learning setting. Hum Centric Comput Inf Sci 5:13. doi:10.1186/s13673-015-0024-3 CrossRefGoogle Scholar
  15. 15.
    Lin K-M (2011) e-Learning continuance intention: moderating effects of user e-learning experience. Comput Educ 56:515–526CrossRefGoogle Scholar
  16. 16.
    Ma R, Oxford RL (2014) A diary study focusing on listening and speaking: the evolving interaction of learning styles and learning strategies in a motivated, advanced ESL learner. System 43:101–113CrossRefGoogle Scholar
  17. 17.
    Mampadi F, Chen SY, Ghinea G, Chen M-P (2011) Design of adaptive hypermedia learning systems: a cognitive style approach. Comput Educ 56:1003–1011CrossRefGoogle Scholar
  18. 18.
    Mustafa YEA, Sharif SM (2011) An approach to Adaptive E-Learning Hypermedia System based on Learning Styles (AEHS-LS): implementation and evaluation. Int J Libr Inf Sci 3(1):15–28Google Scholar
  19. 19.
    Ortigosa A, Paredes P, Rodriguez P (2010) AH-questionnaire: an adaptive hierarchical questionnaire for learning styles. Comput Educ 54(4):999–1005CrossRefGoogle Scholar
  20. 20.
    Ortigosa A, Paredes P, Rodriguez P (2010) AH-questionnaire: an adaptive hierarchical questionnaire for learning styles. Comput Educ 54:999–1005CrossRefGoogle Scholar
  21. 21.
    Özyurt Ö, Özyurt H (2015) Learning style based individualized adaptive e-learning environments: content analysis of the articles published from 2005 to 2014. Comput Hum Behav 52(2015):349–358CrossRefGoogle Scholar
  22. 22.
    Özyurt Ö, Özyurt H, Baki A, Güven B, Karal H (2012) Evaluation of an adaptive and intelligent educational hypermedia for enhanced individual learning of mathematics: a qualitative study. Expert Syst Appl 39:12092–12104CrossRefGoogle Scholar
  23. 23.
    Özyurt Ö, Özyurt H, Baki A, Güven B (2013) Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: implementation and evaluation of UZWEBMAT. Comput Hum Behav 29:726–738CrossRefGoogle Scholar
  24. 24.
    Özyurt Ö, Özyurt H, Baki A (2013) Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: details of UZWEBMAT. Expert Syst Appl 40:2914–2940CrossRefGoogle Scholar
  25. 25.
    Paechter M, Maier B, Macher D (2010) Students’ expectations of, and experiences in e-learning: their relationto learning achievements and course satisfaction. Comput Educ 54:222–229CrossRefGoogle Scholar
  26. 26.
    Park J, Han SH, Kim HK, Seunghwan O, Moon H (2013) Modeling user experience: a case study on a mobile device. Int J Ind Ergon 43:187–196CrossRefGoogle Scholar
  27. 27.
    Peña-Ayala A, Sossa H, Méndez I (2014) Activity theory as a framework for building adaptive e-learning systems: a case to provide empirical evidence. Comput Hum Behav 30:131–145CrossRefGoogle Scholar
  28. 28.
    Pérez Cota M, Thomaschewski J, Schrepp M, Gonçalves R (2014) Efficient measurement of the user experience. A Portuguese version. Proc Comput Sci 27:491–498CrossRefGoogle Scholar
  29. 29.
    Shee DY, Wang Y-S (2008) Multi-criteria evaluation of the web-based e-learning system: a methodology based on learner satisfaction and its applications. Comput Educ 50:894–905CrossRefGoogle Scholar
  30. 30.
    Sun P-C, Tsai RJ, Finger G, Chen Y-Y, Yeh D (2008) What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput Educ 50:1183–1202CrossRefGoogle Scholar
  31. 31.
    Tsai JC, Yen NY (2013) Cloud-empowered multimedia service: an automatic video storytelling tool. J Converg 4(3):13–19Google Scholar
  32. 32.
    van Doorn K, McManus F, Yiend J (2012) An analysis of matching cognitive-behavior therapy techniques to learning styles. J Behav Ther Exp Psychiatry 43:1039–1044CrossRefGoogle Scholar
  33. 33.
    van Seters JR, Ossevoort MA, Tramper J, Goedhart MJ (2012) The influence of student characteristics on the use of adaptive e-learning material. Comput Educ 58:942–952CrossRefGoogle Scholar
  34. 34.
    Weng MM, Shih TK, Hung JC (2013) A personal tutoring mechanism based on the cloud environment. J Converg 4(3):37–44Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Humanitas College of Kyung Hee UniversitySeoulSouth Korea

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