Adaptive Learning Systems: Beyond Teaching Machines

Year 2013, Volume: 4 Issue: 2, 108 - 120, 01.06.2013

Nuri Kara Nese Sevim

Abstract

Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its effects on learning environment. First, after explaining the concepts of teaching machines and adaptive learning systems including their main features as well as integral components, similarities and differences between these technologies are discussed briefly. Then, following the discussion on weaknesses and strengths of adaptive learning systems, what instructional designers should consider in developing and using them are mentioned.

Keywords

Adaptive learning, Adaptive learning systems, Teaching machines, Instructional design, Instructional technology

References

• Atif, Y., Benlamri, R., & Berri, J. (2003). Learning objects based framework for self-adaptive learning. Education and Information Technologies, 8(4), 345–368.
• Brusilovsky, P. (1999). Adaptive and intelligent technologies for web-based education. In C. Rollinger & C. Peylo (Eds.), Special issue on intelligent systems and teleteaching (Vol. 4, pp. 19–25), Künstliche Intelligenz.
• Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User Adapted Interaction, 11(1-2), 87–110.
• Brusilovsky, P. & Nijhawan, H. (2002). A framework for adaptive e-learning based on distributed re-usable learning activities. Proceedings of the World Conference on E Learning, E-Learn 2002, Montreal, Canada.
• Calvin, A. D. (1969). Programmed instruction. Bold New Venture: Indiana University Press.
• Casas, M. (2002). The use of Skinnerian teaching machines and programmed instruction in the United States 1960-1970 (Unpublished doctoral dissertation). Harvard University, 1997. (ERIC Document Reproduction Service No. ED 469942).
• Chieu, V. M. (2005). Constructivist learning: An operational approach for designing adaptive learning environments supporting cognitive flexibility (Unpublished doctoral dissertation). Louvain-la-Neuve, BE: Université catholique de Louvain.
• Clark, R. C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning: Evidence-based guidelines to manage cognitive load. San Francisco, CA: Pfeiffer.
• Dagger, D., Wade, V., & Conlan, O. (2005). Personalisation for all: Making adaptive course composition easy. Educational Technology & Society, 8(3), 9–25.
• Dalgarno, B. (2001). Interpretations of constructivism and consequences for Computer Assisted Learning. British Journal of Educational Technology, 32(2), 183–194.
• Driscoll, M. P. (2005). Psychology of learning for instruction (3nd ed). Boston, MA: Allyn & Bacon.
• Ertmer, P. A. & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50 – 72.
• Federico, P. A. (1999). Hypermedia environments and adaptive instruction. Computers in Human Behavior, 15, 653–692.
• Fischman, J. (2011). The rise of teaching machines, Retrieved on May 23, 2011 from http://chronicle.com/article/The-Rise-of-Teaching-Machines/127389/
• Francois, C. (2011). What is adaptive learning? Retrieved on 24 May 2011 from http://www. wisegeek.com/what-is-adaptive-learning.htm
• Holland, J. G. (1960). Teaching machines: An application of principles from the laboratory. Journal of the Experimental Analysis of Behavior, 3, 275-287.
• Martinez, M. (2003). Adaptive learning. Retrieved on May 24, 2011 from http://www. trainingplace.com/source/research/adaptivelearning.htm
• McKeachie, W. J. (1974). The decline and fall of the laws of learning. Educational Researcher, 128(3), 7–11.
• Melis, E., Andrès, E., Büdenbender, J., Frischauf, A., Goguadze, G., Libbrecht, P., Pollet, M., & Ullrich, C., (2001). ActiveMath: A generic and adaptive web-based learning environment. International Journal of Artificial Intelligence in Education, 12, 385-407.
• Mödritscher, F., Garcia-Barrios, V. M., & Gütl, C. (2004). The past, the present and the future of adaptive e-learning. An approach within the scope of the research project AdeLE. In M. Auer & U. Auer (Eds.), Proceedings of the International Conference on Interactive Computer Aided Learning (ICL 2004). Villach, Austria: Carinthia Tech Institute.
• Nguyen, L. & Do, P. (2008) Learner model in adaptive learning. Proceedings of World Academy of Science, Engineering and Technology, 35, 396-401.
• Paramythis, A.& Loidl-Reisinger, S. (2004). Adaptive learning environments and elearning standards. Electronic Journal on e-Learning, 2(1), 181–194.
• Retalis, R. & Papasalouros, A. (2005). Designing and generating educational adaptive hypermedia applications. Educational Technology & Society, 8(3), 26 – 35.
• Saettler, P. (1990). The evolution of American educational technology. Englewood, CO: Libraries Unlimited.
• Skinner, B. F. (1958). Teaching machines. Science, 128, 969–977.
• Skinner B. F. (1960). Teaching machines. The Review of Economics and Statistics, 42, 189 – 191.
• Stolurow, L. M. & Davis, D. (1965). Teaching machines and computer-based systems. In R. Glaser (Ed.), Teaching machines and programmed learning II: Data and directions (pp. 162-212). Washington, D.C.: National Education Association of the United States.
• Stoyanov, S. & Kirschner, P. (2004) Expert concept mapping method for defining the characteristics of adaptive e-learning: ALFANET project case. Educational Technology Research and Development, 52(2), 41–56.
• Correspondence: Nuri Kara, Research Assistant, Department of Computer Education and
• Instructional Technologies, Faculty of Education, Middle East technical University, Ankara, Turkey