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Rule based adaptive user interface for adaptive E-learning system

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Abstract

The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of the learners. Learning style of the learner defines the way of learning the contents. The system should not only meet the individual need of the contents but also the customized user interface on the portal. Hence, an AES should mainly focus on recommending learning contents with Adaptive User Interface (AUI) on the portal. The work in the paper proposes a generic approach to provide the learning contents with AUI components based on the learning styles of the learners. The learning style adopted for the work is the Felder-Silverman Learning Style Model (FSLSM). The proposed approach defines generic rules which are generated automatically for any online course with the adaptive contents. Also, the approach takes care of new learners by providing learning path as a user interface component on the portal. The experiment has been conducted on engineering students for a particular online course. The portal is validated using parameters of usability testing by generating test cases and statistical analysis has been carried out to identify the impact of AUI components on the learning process. The result shows the well adaptation of user interface components and contents based on learning styles.

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References

  • Abraham, G., Balasubramanian, V., & Saravanaguru, R. K. (2013). Adaptive e-learning environment using learning style recognition. International Journal of Evaluation and Research in Education (IJERE), 2(1), 23–31.

    Article  Google Scholar 

  • Ahmad A-R., Basir, O., & Hassanein, K. (2004). Bluesky cloud frame- work: An e-learning framework embracing cloud computing. In Proceedings of the 4th International Conference on Electronics Business, pages 925–934. ISBN 978-3-642-10664-4.

  • Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: a conceptual framework. Procedia Social and Behavioral Sciences, 2(2), 5755–5761.

    Article  Google Scholar 

  • Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, Kluwer Academic Publishers, 11, 87–110.

    Article  MATH  Google Scholar 

  • Brusilovsky, P., & Sosnovsky, S. (2005). Individualized exercises for self-assessment of programming knowledge: An evaluation of QuizPACK. Journal on Educational Resources in Computing (JERIC), 5(3).

  • Brusilovsky, P., Wade, V. P., & Conlan, O. (2008). From learning objects to adaptive content services for e-learning. Architecture Solutions for E-Learning Systems 243–261.

  • De Bra, P., Aroyo, L., & Cristea, A (2004). Adaptive web-based educational hypermedia. Web Dynamics. Springer, Berlin, Heidelberg, 387–410.

  • Despotovic-Zrakic, M., Markovic, A., Bogdanovic, Z., Barac, D., & Krco, S. (2012). Providing adaptivity in moodle lms courses. Jour- nal of Educational Technology and Society, 15(1), 326–338.

    Google Scholar 

  • Doelitzscher, F., Sulistio, A., Reich, C., Kujis, H., & Wolf, D. (2014). A proposed architectural model for an automatic adaptive e-learning system based on users learning style. International Journal of Advanced Computer Science and Applications, 5(4), 1–5.

    Google Scholar 

  • Fouad, K. M. (2012). Proposed approach to build semantic learner model in adaptive e-learning. International Journal of Computer Applications, 58(17).

  • Graf S., & Kinshuk (2007). Providing adaptive courses in learning management systems with respect to learning styles. In Proceedings of the world conference on e-learning in corporate, government, healthcare, and higher education (e-Learn), pp. 2576–2583.

  • Hong, H., & Kinshuk, D. (2004). Adaptation to student learning styles in web based educational systems. EdMedia: World Conference on Educational Media and Technology, 14, 491–496.

    Google Scholar 

  • Kaewkiriya, T., Utakrit, N., & Tiantong, M. (2016). The design of a rule base for an e-learning recommendation system based on multiple intelligences. International Journal of Information and Education Technology, 6(3), 206–210.

    Article  Google Scholar 

  • Karagiannidis, C., & Sampson, D. (2004). Adaptation rules relating learning styles research and learning objects meta-data. In Workshop on Individual Differences in Adaptive Hypermedia. 3rd International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (AH2004), Eindhoven, Netherlands, pages 34–42. Citeseer.

  • Lazarinis, F., Green, S., & Pearson, E. (2010). Creating personalized assessments based on learner knowledge and objectives in a hypermedia Web testing application. Computers & Education, 55(4), 1732–1743.

    Article  Google Scholar 

  • Liyanage P. P., Gunawardena L., & Hirakawa M. (2014). Using learning styles to enhance learning management systems. International Journal on Advances in ICT for Emerging Regions, 7(2), 1–10.

    Google Scholar 

  • Liyanage P. P., Lasith Gunawardena, & Hirakawa M. (2016). Detecting learning styles in learning management systems using data mining. Journal of Information Processing, 24(4), 740–749.

  • Mamat, N., & Yusof, N. (2013). Learning style in a personalized collaborative learning framework. Procedia-Social and Behavioral Sciences, 103, 586–594.

    Article  Google Scholar 

  • Nafea, S., Maglaras, L., Siewe, F., Smith, R., & Janicke, H. (2016). Personalized student’s profile based on ontology and rule-based reasoning. EAI Transactions on E-learning, 11(1).

  • Park, H., & Song, H.-D. (2015). Make e-learning effortless! Impact of a re-designed user interface on usability through the application of an affordance design approach. International Journal of Information and Education Technology, 18(3), 185–196.

    Google Scholar 

  • Paulheim, H. (2010). Integration of user interface components based on ontologies and rules. SAP Research Center Darmstadt, Germany 355–376.

  • Popescu, E., Badica, C., & Moraret, L. (2010). Accommodating learning styles in an adaptive educational system. Journal of Computing and Informatics, 34(4), 451–462.

    Google Scholar 

  • Premlatha, K. R., & Geetha, T. V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: A survey. Artificial Intelligence Review, 44(4), 443–465.

    Article  Google Scholar 

  • Webster, R. (2009). Interfaces for e-learning: cognitive styles and software agents for web-based learning support. International Journal of e-learning 89–104.

  • Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an adaptive learning system with multiple perspectives based on students learning styles and cognitive styles. Journal of Educational Technology and Society, 16(4), 185–200.

    Google Scholar 

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Correspondence to Radhika M. Pai.

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Kolekar, S.V., Pai, R.M. & M. M., M.P. Rule based adaptive user interface for adaptive E-learning system. Educ Inf Technol 24, 613–641 (2019). https://doi.org/10.1007/s10639-018-9788-1

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