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Seamless Web-Mediated Training Courseware Design Model: Innovating Adaptive Educational-Learning Systems

  • Elspeth McKay
  • John Izard
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 17)

Abstract

We present an innovative Web-mediated training system design architecture that encourages novice courseware developers to deliver their own adaptive (user-centered) educational-learning systems (AELS) by utilizing achievable and cost effective online training modules. We propose a seamless Web-mediated training system design architecture that includes a choice of intelligent agents (personal Avatars) to guide the trainee through their knowledge acquisition. We cut through the more usual information systems (IS) development rhetoric. Instead of cloaking the courseware design process in highly technical mystery, we argue that educational technology experts should encourage non-technical developers to believe that the possibility of customizing their own Web-mediated training programs falls within their grasp. The preliminary findings from a pilot study conducted to test our AELS as an in-house courseware development tool indicate that it will be most suitable for corporate and government training courseware creators.

Keywords

Information System Learn Management System Information Communication Technology Authoring Tool Uniform Resource Identifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Alonso, G., Casati, F., Kuno, H., Machiraju, V.: Web services: Concepts, architectures and applications. Springer, CA (2004)zbMATHGoogle Scholar
  2. Anderson, T. (ed.): Theory and practice of online learning, Athabasca University (2008), http://www.aupress.ca/index.php/books/120146 (accessed November 5, 2011)
  3. Anderson, N., Lankshear, C., Courtney, L., Timms, C.: Girls and ICT survey: Initial findings, Curriculum Leadership Journal, http://Cmslive.Curriculum.Edu.Au/Leader/Default.Asp?Id=13812 (accessed November 5, 2007)
  4. Blazhenkova, O., Kozhevnikov, M.: The new object-spatial-verbal cognitive style model: Theory and measurement. Applied Cognitive Psychology 23(5), 638–663 (2009)CrossRefGoogle Scholar
  5. Clark, R.C., Kwinn, A.: The new virtual classroom: Evidence-based guidelines for synchronous e-Learning. Wiley, CA (2007)Google Scholar
  6. Cohen, F.: Using Web services for e-Commerce sign-in: SOAP authentication for distributed computers (2002), http://ibm.com/developerworks/webservices/library/ws-single/ (accessed November 5, 2011)
  7. Dasgupta, S., Granger, M., McGarry, N.: User acceptance of e-collaboration technology: An extension of the technology acceptance model. In: Group Decision and Negotiation, vol. 11, pp. 87–100. Kluwer, The Netherlands (2002)Google Scholar
  8. Castro, E.: HTML for the world wide web: Visual quickstart guide, 4th edn. Peachpit Press, CA (2000)Google Scholar
  9. Corbitt, A.T., Koedinger, K.R., Anderson, J.R.: Intelligent tutoring systems. In: Helander, M., Landauer, T.K., Prabhu, P.V. (eds.) Handbook of human-computer interaction, 2nd edn., pp. 849–874. Elsevier Science B.V., Amsterdam (1997)Google Scholar
  10. Dick, G.N., Case, T.L., Ruhlman, P., Van Slyke, C., Winston, M.: On-line learning in the business environment. Communications of the Association for Information Systems 17(41), 895–904 (2006)Google Scholar
  11. Driscoll, M.M.: Developing synchronous web-based training for adults in the workplace. In: Khan, B.H. (ed.) Web-Based Training, pp. 173–183. Educ. Tech. Publication, NJ (2001)Google Scholar
  12. Forbus, K.D., Feltovich, P.J.: Smart Machines in Education. MIT Press, UK (2001)Google Scholar
  13. Gery, G.: Making CBT happen: Prescriptions for successful implementation of computer-based training in your organization. Harper & Row, NY (1987)Google Scholar
  14. Jacobs, I., Walsh, N.(eds.): Architecture of the world wide web, 1, http://www.w3.org/TR/webarch/ (accessed November 5, 2011)
  15. Jameson, A.: Adaptive interfaces. In: Sears, A., Jacko, J.A. (eds.) The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pp. 433–458. CRC Press, NY (2008)Google Scholar
  16. McKay, E.: Instructional Strategies Integrating the Cognitive Style Construct: A Meta-Knowledge Processing Model (Contextual Components That Facilitate Spatial/Logical Task Performance). PhD Dissertation, Deakin University, http://tux.lib.deakin.edu.au/adt-VDU/public/adt-VDU20061011.122556/ (accessed November 5, 2011)
  17. McKay, E.: Planning effective HCI to enhance access to educational applications. International Journal Universal Access in the Information Society 6(1), 77–85 (2007)CrossRefGoogle Scholar
  18. McKay, E.: The human-dimensions of human-computer interaction: Balancing the HCI equation. IOS Press, Amsterdam (2008)Google Scholar
  19. McKay, E., Axmann, M., Banjanin, N., Howat, A.: Towards web-mediated learning reinforcement: Rewards for online mentoring through effective human-computer interaction. In: Proceedings of IASTED (2007)Google Scholar
  20. McKay, E., Martin, J.: Multidisciplinary collaboration to unravel expert knowledge: Designing for effective human-computer interaction. In: Keppell, M. (ed.) Instructional Design: Case Studies in Communities of Practice, pp. 309–329. Idea Group, UK (2009)Google Scholar
  21. McKay, E., Merrill, M.D.: Cognitive skill and Web-based educational systems. In: McKay, E. (ed.) eLearning Conference on Design and Development: Instructional Design - Applying first principles of instruction. Australasian Publications On-Line, Informit Library (2003), http://www.informit.com.au/library/96-108 (accessed November 5, 2011)
  22. Merrill, M.D.: Pebble-in-the-pond model for instructional development. Performance Measurement (2002), http://www.ispi.org/pdf/Merrill.pdf (accessed November 5, 2011)
  23. Merrill, M.D., Tennyson, R.D., Posey, L.O.: Teaching concepts: An instructional design guide, 2nd edn. Educational Technology Publications, New Jersey (1992)Google Scholar
  24. Peng, H., Chou, C., Chang, C.-Y.: From virtual environments to physical environments: Exploring interactivity in ubiquitous-learning systems. Educational Technology & Society 11(2), 54–66 (2008)Google Scholar
  25. Pohl, H.-M., Deicke, B., Milde, J.-T.: From Paper to Module – An Integrated Environment for Generating SCORM Compliant Moodle Courses Out of Text and Multimedia Elements. In: Jacko, J.A. (ed.) HCI International 2009, Part IV. LNCS, vol. 5613, pp. 196–203. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. Schank, R.C.: Designing world-class e-Learning: How IBM, GE, Harvard Business School, & Columbia University are succeeding at e-learning. McGraw-Hill, New York (2002)Google Scholar
  27. The Open Group. Introduction to single-sign-on (2008), http://www.opengroup.org/security/sso/ (accessed November 5, 2011)
  28. Zapata-Rivera, J.-D., Greer, J.: Inspectable Bayesian student modelling servers in multi-agent tutoring systems. International Journal of Human-Computer Studies 61(4), 535–563 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Business Information Technology and LogisticsRMIT UniversityMelbourneAustralia
  2. 2.School of EducationRMIT UniversityMelbourneAustralia

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