Conceptual design of the new generation adaptive learning management system

  • Authors

    • Aleksandrs Gorbunovs
    • Zanis Timsans
    • Bruno Zuga
    • Viktors Zagorskis
    https://doi.org/10.14419/ijet.v7i2.28.12894

    Received date: May 16, 2018

    Accepted date: May 16, 2018

    Published date: May 16, 2018

  • Adaptive information system, Feedback, Learning analytics, Personalized learning.
  • Abstract

    Taking into account that each new day an amount of daily processed data grows exponentially, it is obvious that knowledge society needs flexible, effective and high performing tools to retrieve, learn and apply necessary information. Learning management systems become more and more intelligent and adaptive. Learning analytics instruments give course developers a possibility to assess and analyze learners’ activities and behavior patterns within e-learning environment, allowing to propose them personalized self-paced learning path and types of learning objects. This paper discusses challenges in development of adaptive learning management system and outlines its prospective models and properties.

  • References

    1. UNESCO, World report: Towards knowledge societies. Paris: UNESCO Publishing, (2005), pp.1-220, available online: http://unesdoc.unesco.org/images/0014/001418/141843e.pdf, last visit: 01.02.2018.
    2. Sivakumar S, Venkataraman S & Gombiro C, “A user-intelligent adaptive learning model for learning management system using data mining and artificial intelligence”, International Journal for Innova-tive Research in Science and Technology, Vol.1, No.10, (2015), pp.78–81.
    3. Bates AW (Tony), Teaching in a digital age: guidelines for design-ing teaching and learning, Ontario: Contact North, (2016), pp.1-509, available online: https://teachonline.ca/sites/default/files/pdfs/teaching-in-a-digital-age_2016.pdf, last visit: 14.02.2018.
    4. Sclater N, Peasgood A & Mullan J, Learning analytics in higher ed-ucation: a review of UK and international practice. Full report, UK, Bristol: Jisc, (April 2016), pp.1-40, available online: https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf, last visit: 15.02.2018.
    5. Schumacher C & Ifenthaler D, “Features students really expect from learning analytics”, Proceedings of the 13th International Conference on Cognition and Exploratory Learning in Digital Age, (2016), pp.67-76.
    6. Worsley M & Blikstein P, “Towards the development of multimodal action based assessment”, Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK '13), Leuven, Belgium, April 08 - 13, 2013, New York: ACM, (2013), pp.94-101.
    7. Shacklock X, From bricks to clicks - the potential of data and ana-lytics in higher education. UK: Higher Education Commission, (January 26, 2016), pp.1-76. available online: http://www.policyconnect.org.uk/hec/sites/site_hec/files/report/419/fieldreportdownload/frombrickstoclicks-hecreportforweb.pdf, last visit: 30.01.2018.
    8. Brusilovsky P, Methods and Techniques of Adaptive Hypermedia. In Brusilovsky P, Kobsa A & Vassileva J (Eds.), Adaptive Hypertext and Hypermedia. Dordrecht: Kluwer Academic Publishers, (1998), pp.1-43.
    9. Leone S, Characterisation of a personal learning environment as a lifelong learning tool. New York: Springer-Verlag, (2013), pp.1-88, doi: 10.1007/978-1-4614-6274-3.
    10. Graf S, Liu T & Kinshuk C, “Analysis of learners’ navigational be-haviour and their learning styles in an online course”, Journal of Computer Assisted Learning, Vol.26, No.2, (2010), pp.116-131, doi:10.1111/j.1365-2729.2009.00336.
    11. Danielson R, “Learning styles, media preferences, and adaptive ed-ucation”, Proceedings of Workshop “Adaptive Systems and User Modeling on the World Wide Web” at the 6th International Confer-ence on User Modeling, Chia Laguna, Sardinia, Italy, (1997), pp.31-35.
    12. Gilbert JE & Han CY, “Arthur: adapting instruction to accommo-date learning style”, Proceedings of World Conference of the WWW and Internet, Honolulu, HI, (1999), pp.433-438.
    13. Brusilovsky P, “Adaptive Hypermedia”, User Modeling and User-Adapted Interaction, Vol.11, No.1-2, (2001), pp.87-110.
    14. Oppermann R, Introduction. Adaptive user support, Hillsdale, New Jersey: Lawrence Erlbaum Associates, (1994), pp.1-13.
    15. Vagale V & Niedrite L, “Learner model’s utilization in the e-learning environments”, Local Proceedings and Materials of Doc-toral Consortium of the Tenth International Baltic Conference on Databases and Information Systems, Vilnius, (July 8-11, 2012), pp.162-174.
    16. Ragusa C, Hoffman M & Leonard J, “Unwrapping GIFT: a primer on developing with the generalized intelligent framework for tutor-ing”, Proceedings of the workshops, 16th International Conference on Artificial Intelligence in Education, Memphis, USA, Vol.7, (July 9-13, 201uy3), pp.10-19.
    17. Sottilare R, “Challenges in authoring, instructional management, and evaluation methods for adaptive instructional systems”, Con-ference “Technology, Instruction, Cognition & Learning Special In-terest Group”, Symposium on Intelligent Tutoring Systems, Big Da-ta-Learning Analytics, and Automated Humanlike Tutoring: Similar-ities and Differences, San Antonio, (April 28, 2017), pp.1-6.
    18. Gallagher PS, “The total learning architecture (TLA): learning across applications”, Abstracts of Human Systems Conference “Achieving the Third Offset: Maximizing Human-Machine Symbiosis”, Spring-field, VA, USA, (March 7-8, 2017), pp.1-24.
    19. Anohina-Naumeca A, “Determining the set of concept map based tasks for computerized knowledge self-assessment”, Procedia - So-cial and Behavioral Sciences, Vol. 69, (2012), pp.143-152.
    20. Panjaburee P, Hwang GJ, Triampo W & Shih BY, “A multi-expert approach for developing testing and diagnostic systems based on the concept-effect model”, Computers & Education, Vol.55, No.2, (September 2010), pp.527-540.
    21. Graudina V & Grundspenkis J, “Concept map generation from OWL ontologies”, Proceedings of the 3rd International Conference on Concept Mapping, OU Vali Press, Estonia, (2008), pp.173-180.
    22. Dzelzkaleja L, “Real-time color codes for assessing learning pro-cess”, Proceedings of the International Conference “Meaning in Translation: Illusion of Precision”, May 11-13, 2016, Riga, Proce-dia - Social and Behavioral Sciences, Vol.231, (2016), pp.263-269, doi: 10.1016/j.sbspro.2016.09.101.
    23. Dzelzkaleja L, “Real time color codes in a classroom”, Proceedings of the 9th International Conference on Computer Supported Educa-tion (CSEDU-2017), Porto, (April 21-23, 2017), pp.111-117, doi: 10.5220/0006357201600165.
    24. Dzelzkaleja L & Timsans Z, “Colour codes method digitalization in edX e-learning platform”, Proceedings of the 10th International Conference on Computer Supported Education (CSEDU-2018), 2018, in press.
    25. Kapenieks A, et al., “Piloting eBig3: a tripple-screen e-learning ap-proach”, Proceedings of the 6th International Conference on Com-puter Supported Education (CSEDU 2014), Barcelona, Vol.1, (1-3 April, 2014), pp.325-329, doi:10.5220/0004848603250329.
    26. Kapenieks A, et al., “User behavior in multi-screen eLearning”, Proceedings of the Int. Conf. on Communication, Management and Information Technology (ICCMIT 2015), Procedia - Computer Sci-ence, Vol.65, (2015), pp.761-767, doi: 10.1016/j.procs.2015.09.021.
    27. Imrie P, “Virtual personal assistants - a different approach to sup-porting the end user”, Proceedings of the 3rd International Work-shop on Socio-Technical Perspective in IS development, Essen, (June 13, 2017), pp.106-109.
  • Downloads

  • How to Cite

    Gorbunovs, A., Timsans, Z., Zuga, B., & Zagorskis, V. (2018). Conceptual design of the new generation adaptive learning management system. International Journal of Engineering and Technology, 7(2.28), 129-133. https://doi.org/10.14419/ijet.v7i2.28.12894