Interoperable Intelligent Tutoring Systems as SCORM Learning Objects

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 17)

Abstract

Learning technologies are currently present in many educational institutions around the world. Learning Management Systems (LMS), Personal Learning Environments (PLE) and other types of educational platforms are very popular and now common in our schools and universities. However, most of the educational content currently available in the educational platforms is non-adaptive and non-intelligent educational content such as HTML pages, PDF files and Power Point Presentations (PPT). This type of content does not provide the high quality educational assistance that technology can provide. On the other hand, intelligent and adaptive educational systems are a successful and mature field of learning technologies that can provide very high quality educational assistance. In order to allow Intelligent Tutoring systems (ITS) to be loaded into different types of educational systems, we have developed an approach based on E-Learning standards. Our approach is also grounded in a very well known paradigm for implementing ITS, and the main goal of this chapter is to present a novel approach for implementing ITS as learning objects using the Sharable Content Object Reference Model (SCORM).

Keywords

Outer Loop Task Selection Learn Management System Intelligent Tutoring System Educational Content 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ADL. ADL - Who We Are, http://www.adlnet.gov/About/Pages/Default.aspx (acessed February 01, 2011)
  2. ADL. SCORM, http://www.adlnet.gov/Technologies/scorm (acessed February 01, 2011)
  3. ADL. SCORM Benefits, http://www.adlnet.gov/Documents/SCORM%20FAQ.aspx#scormq2 (acessed February 01, 2011)
  4. Aleven, V., McLaren, B.M., Sewall, J.: Scaling up programming by demonstration for intelligent tutoring systems development: An open-access website for middle-school mathematics learning. IEEE Transactions on Learning Technologies 2(2), 64–78 (2009)CrossRefGoogle Scholar
  5. Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: A new paradigm for intelligent tutoring systems: Example-Tracing Tutors. International Journal of Artificial Intelligence in Education 19(2), 105–154 (2009)Google Scholar
  6. Anderson, J.R.: Rules of the mind. Erlbaum, Hillsdale (1993)Google Scholar
  7. Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: Lessons learned. The Journal of the Learning Sciences 4(2), 167–207 (1995)CrossRefGoogle Scholar
  8. Beatty, B., Ulasewicz, C.: Faculty perspectives on moving from blackboard to the Moodle learning management system. TechTrends: Linking Research & Practice to Improve Learning 50(4), 36–45 (2006)Google Scholar
  9. Brusilovsky, P.: The construction and application of student models in intelligent tutoring systems. Journal of Computer and Systems Sciences International 32(1), 70–89 (1994)Google Scholar
  10. Brusilovsky, P., Wade, V., Conlan, O. (2007). From learning objects to adaptive content services for E- Learning. In: Architecture Solutions for E-Learning Systems. IGI Global, USA (2007)Google Scholar
  11. Corbett, A.T., Anderson, J.R.: The LISP intelligent tutoring system: Research in skill acquisition. In: Larkin, J., Chabay, R., Scheftic, C. (eds.) Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing Communication and Collaboration. Erlbaum, Hillsdale (1992)Google Scholar
  12. Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1994)CrossRefGoogle Scholar
  13. Corbett, A.T., McLaughlin, M., Scarpinatto, K.C.: Modeling student knowledge: cognitive tutors in High School and College. In: Proceedings of UMUAI, pp. 81–108 (2000)Google Scholar
  14. Dahl, O.-J., Nygaard, K.: SIMULA: an ALGOL-based simulation language. Commun. ACM 9, 671–678 (1966)MATHCrossRefGoogle Scholar
  15. De Bra, P., Smits, D., van der Sluijs, K., Cristea, A., Hendrix, M.: GRAPPLE: Personalization and Adaptation in Learning Management Systems. In: Proceedings of WCEMHT, pp. 3029–3038 (2010)Google Scholar
  16. Devedzic, V., Harrer, A.: Software patterns in ITS architectures. International Journal of Artificial Intelligence in Education 15(2), 63–94 (2005)Google Scholar
  17. Downes, S.: E-learning 2.0. In: Proceedings of e-Learn. (2005)Google Scholar
  18. Hrastinski, S.: Asynchronous & Synchronous E-Learning. EDUCAUSE 31(4), 51–55 (2008)Google Scholar
  19. Koedinger, K.R., Corbett, A.T.: Cognitive tutors: Technology bringing learning science to the classroom. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 61–78. Cambridge University Press (2006)Google Scholar
  20. Lovett, M.C.: Cognitive Task Analysis in Service of Intelligent Tutoring System Design: A Case Study in Statistics. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 234–243. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. Rey, M.L., Fernanndez, A., Diaz, V.R., Pazos, J.A.: Providing SCORM with adaptivity. In: Proceedings of ICWWW, pp. 981–982 (2006)Google Scholar
  22. McCarthy, J.E.: Military Applications of adaptive training technology. In: Lytras, D.G., Ordóñez de Pablos, P., Huang, W. (eds.) Technology Enhanced Learning: Best Practices, pp. 304–347. IGI Global (2008)Google Scholar
  23. Mia, S.: The Difficulties in web-based tutoring, and some possible solutions. In: Proceedings of the Workshop of IESWWW (1997)Google Scholar
  24. Psotka, J., Massey, L., Mutter, S.: Intelligent tutoring systems: lessons learned. Lawrence Erlbaum Associates, Hillsdale (1988)Google Scholar
  25. Rey-López, M., Brusilovsky, P., Meccawy, M., Díaz-Redondo, R., Fernández-Vilas, A., Ashman, H.: Resolving the problem of intelligent learning content in learning management systems. International Journal on E-Learning 7(3), 363–381 (2008)Google Scholar
  26. Rey-López, M., Díaz-Redondo, R.P., Fernández-Vilas, A., Pazos-Arias, J.J., García-Duque, J., Gil-Solla, A., Ramos, C.M.: An extension to the ADL SCORM standard to support adaptivity: The t-learning case-study. Computer Standards & Interfaces 31(2), 309–318 (2009)CrossRefGoogle Scholar
  27. Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.: Cognitive tutor: Applied research in mathematics education. American Psychonomic Bulletin & Review 14(2), 249–255 (2007)CrossRefGoogle Scholar
  28. Sabbir-Ahmed, K.: A conceptual framework for web-based intelligent learning environments using SCORM-2004. In: Proceedings of IEEE ICALT, pp. 12–15 (2004)Google Scholar
  29. Santos, G., Figueira, A.: Reusable and inter-operable web-based intelligent tutoring systems using SCORM 2004. In: Proceedings of ECe-L, pp. 521–528 (2010a)Google Scholar
  30. Santos, G., Figueira, A.: Web-based intelligent tutoring systems using the SCORM 2004 specification: A conceptual framework foriImplementing SCORM compliant intelligent web-based learning environments. In: Proceedings of ICALT, pp. 676–678 (2010b)Google Scholar
  31. Santos, G., Figueira, A.: Intelligent tutoring systems with SCORM. The European Journal for the Informatics Professional 7(2), 34–42 (2011)Google Scholar
  32. Schraagen, J., Chipman, S., Shalin, V., Shalin, V.: Cognitive task analysis. L. Erlbaum Associates (2000)Google Scholar
  33. Software, R. SCORM Content packaging (2011), http://scorm.com/scorm-explained/technical-scorm/content-packaging/ (acessed November 26, 2010)
  34. van Harmelen, M.: Design trajectories: four experiments in PLE implementation. Interactive Learning Environments 16(1), 35–46 (2008)CrossRefGoogle Scholar
  35. Vanlehn, K.: The Behavior of tutoring systems. International Journal of Artificial Intelligence on Education 16(3), 227–265 (2006)Google Scholar
  36. Vanlehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes physics tutoring system: five years of evaluations. In: Proceedings of ICAIE, pp. 678–685 (2005)Google Scholar
  37. Watkins, R.: E-Learning. Handbook of improving performance in the workplace: selecting and implementing performance interventions, pp. 577–597. John Wiley & Sons, Inc. (2010)Google Scholar
  38. White, S.: Higher education and learning technologies an organisational perspective. ECS, University of Southampton (2006)Google Scholar
  39. Wijekumar, K., Meyer, B., Felix, D., Walker, D.: Creating web-based intelligent tutoring systems using the. net infrastructure: A case study. In: Proceedings of WCE-LCGHHE, pp. 2494–2497 (2003)Google Scholar
  40. Wiley, D. (ed.): The instructional use of learning objects. Association for Educational Communications and Technology (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Instituto Superior TécnicoTechnical University of LisbonLisbonPortugal
  2. 2.INESC-IDLisbonPortugal

Personalised recommendations