Adaptive learning technologies provide an environment that intelligently adjusts to a ­learner’s needs by presenting suitable information, instructional materials, feedback and recommendations based on one’s unique individual characteristics and situation. This chapter first focuses on the concept of adaptivity based on four types of learner differences that can be used by adaptive technologies: learning styles, cognitive abilities, affective states and the current learning context/situation. In order to provide adaptivity, the characteristics of learners need to be known first. Therefore, this chapter discusses methods for identifying learners’ individual differences as well as how the information about these individual ­differences can be used to provide learners with adaptive learning experiences. Furthermore, the chapter demonstrates how adaptivity can be provided in different settings, focusing on both desktop-based learning and mobile/pervasive/ubiquitous learning. Finally, open issues in adaptive technologies are discussed and future research directions are identified.


Affective states Cognitive abilities Context Context modeling Learning styles Student modeling 



The authors also acknowledge the support of NSERC, iCORE, Xerox, and the research-related gift funding by Mr. A. Markin.


  1. Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia environment: Empirical evaluation. In P. de Bra, H. C. Davis, J. Kay, & M. Schraefel (Eds.), Proceedings of the Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 41–52). Nottingham: Eindhoven University.Google Scholar
  2. Blackboard. (2011). Retrieved 30 March, 2011, from
  3. *Brusilovsky, P. (1996). Methods and techniques of adaptive ­hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129.Google Scholar
  4. *Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11, 87–110.Google Scholar
  5. Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: A tool for developing adaptive courseware. Computer Networks and ISDN Systems, 30(1–7), 291–300.CrossRefGoogle Scholar
  6. Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M., & Lee, J.-H. (2006). Learning style diagnosis based on user interface behavior for the customization of learning interfaces in an intelligent tutoring system. In M. Ikeda, K. D. Ashley, & T.-W. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (Lecture notes in computer science, Vol. 4053, pp. 513–524). Berlin: Springer.CrossRefGoogle Scholar
  7. Chang, A., & Chang, M. (2006). Creating an adaptive mobile navigation learning path for elementary school students’ remedy education. Proceedings of the International Conference on Interactive Computer Aided Learning. Villach, Austria.Google Scholar
  8. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles? What research has to say to practice. London: Learning and Skills Research Centre/University of Newcastle upon Tyne.Google Scholar
  9. D’Mello, S., Craig, S., Fike, K., & Graesser, A. (2009). Responding to learners’ cognitive-affective states with supportive and shakeup dialogues. Proceedings of the International Conference on Human-Computer Interaction. Lecture notes in computer science (Vol. 5612, pp. 595–604). Berlin: SpringerGoogle Scholar
  10. Dagger, D., Wade, V., & Conlan, O. (2005). Personalisation for all: Making adaptive course composition easy. Educational Technology & Society, 8(3), 9–25.Google Scholar
  11. de Bra, P., & Calvi, L. (1998). AHA! An open adaptive hypermedia architecture. The New Review of Hypermedia and Multimedia, 4(1), 115–139.CrossRefGoogle Scholar
  12. de Bra, P., Smits, D., van der Sluijs, K., Cristea, A., & Hendrix, M. (2010). GRAPPLE: Personalization and adaptation in learning management systems. Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications (ED-Media) (pp. 3029–3038). Chesapeake, VA: AACE.Google Scholar
  13. Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004). The impact of childhood intelligence on later life: Following up the Scottish mental surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130–147.CrossRefGoogle Scholar
  14. Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7.CrossRefGoogle Scholar
  15. El-Bishouty, M. M., Ogata, H., Ayala, G., & Yano, Y. (2010). Context-aware support for self-directed ubiquitous-learning. International Journal of Mobile Learning and Organisation, 4(3), 317–331.CrossRefGoogle Scholar
  16. El-Bishouty, M. M., Ogata, H., & Yano, Y. (2007). PERKAM: Personalized knowledge awareness map for computer supported ubiquitous learning. Educational Technology & Society, 10(3), 122–134.Google Scholar
  17. Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.Google Scholar
  18. García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794–808.CrossRefGoogle Scholar
  19. Graf, S. (2007). Adaptivity in learning management systems focussing on learning styles. PhD thesis, Vienna University of Technology.Google Scholar
  20. Graf, S., & Kinshuk. (2007). Providing adaptive courses in learning management systems with respect to learning styles. In G. Richards (Ed.), Proceedings of the World Conference on e-Learning in Corporate, Government, Healthcare, and Higher Education (e-Learn 2007) (pp. 2576–2583). Chesapeake, VA: AACE Press.Google Scholar
  21. Graf, S., & Kinshuk. (2008). Adaptivity and personalization in ­ubiquitous learning systems. In A. Holzinger (Ed.), Proceedings of the Symposium on Usability and Human Computer Interaction for Education and Work (USAB 2008) (pp. 331–338). Berlin: Springer.Google Scholar
  22. Graf, S., Kinshuk, & Ives, C. (2010). A flexible mechanism for providing adaptivity based on learning styles in learning management systems. Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2010) (pp. 30–34). Sousse, Tunisia: IEEE Computer Society.Google Scholar
  23. *Graf, S., Kinshuk, & Liu, T. -C. (2009). Supporting teachers in identifying students’ learning styles in learning management systems: An automatic student modelling approach. Educational Technology & Society, 12(4), 3–14.Google Scholar
  24. Graf, S., Liu, T.-C., & Kinshuk. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116–131.CrossRefGoogle Scholar
  25. *Graf, S., Liu, T. -C., Kinshuk, Chen, N. -S., & Yang, S. J. H. (2009). Learning styles and cognitive traits—their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6), 1280–1289.Google Scholar
  26. Graf, S., & Kinshuk. (2013). Dynamic student modelling of learning styles for advanced adaptivity in learning management systems. International Journal of Information Systems and Social Change, 4(1), 85–100.Google Scholar
  27. Graf, S., Yang, G., Liu, T.-C., & Kinshuk. (2009). Automatic, global and dynamic student modeling in a ubiquitous learning environment. International Journal on Knowledge Management and E-Learning, 1(1), 18–35.Google Scholar
  28. Greer, J., McCalla, G., Cooke, J., Collins, J., Kumar, V., Bishop, A., et al. (1998). The Intelligent Helpdesk: Supporting peer-help in a university course. Proceedings of the International Conference on Intelligent Tutoring Systems. Lecture notes in computer science (Vol. 1452, pp. 494–503). Berlin: Springer.Google Scholar
  29. Honey, P., & Mumford, A. (1992). The manual of learning styles (3rd ed.). Maidenhead: Peter Honey.Google Scholar
  30. *Hwang, G. -J., Tsai, C. -C., & Yang, S. J. H. (2008). Criteria, strategies and research issues of context-aware ubiquitous learning. Educational Technology & Society, 11(2), 81–91.Google Scholar
  31. Hwang, G.-J., Yang, T.-C., Tsai, C.-C., & Yang, S. J. H. (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 53(2), 402–413.CrossRefGoogle Scholar
  32. Jia, B., Zhong, S., Zheng, T., & Liu, Z. (2010). The study and design of adaptive learning system based on fuzzy set theory. In: Transactions on edutainment IV. Lecture notes in computer science (Vol. 6250, pp. 1–11). Berlin: Springer.Google Scholar
  33. *Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning, and instruction, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  34. Karampiperis, P., & Sampson, D. G. (2005). Adaptive learning resources sequencing in educational hypermedia systems. Educational Technology & Society, 8(4), 128–147.Google Scholar
  35. Kashihara, A., Kinshuk, Oppermann, R., Rashev, R., & Simm, H. (2000). A cognitive load reduction approach to exploratory learning and its application to an interactive simulation-based learning system. Journal of Educational Multimedia and Hypermedia, 9(3), 253–276.Google Scholar
  36. Khan, F. A., Graf, S., Weippl, E. R., & Tjoa, A. M. (2010). Identifying and incorporating affective states and learning styles in web-based learning management systems. International Journal of Interaction Design & Architectures, 9–10, 85–103.Google Scholar
  37. Kinshuk, & Lin, T. (2003). User exploration based adaptation in adaptive learning systems. International Journal of Information Systems in Education, 1(1), 22–31.Google Scholar
  38. *Kinshuk, & Lin, T. (2004). Cognitive profiling towards formal adaptive technologies in web-based learning communities. International Journal of WWW-based Communities, 1(1), 103–108.Google Scholar
  39. Lin, T., & Kinshuk (2004). Dichotomic node network and cognitive trait model. Proceedings of IEEE International Conference on Advanced Learning Technologies (pp. 702–704). Los Alamitos, CA: IEEE Computer Science.Google Scholar
  40. Lu, H., Jia, L., Gong, S.-H., & Clark, B. (2007). The relationship of kolb learning styles, online learning behaviors and learning outcomes. Journal of Educational Technology & Society, 10(4), 187–196.Google Scholar
  41. Martín, S., Sancristobal, E., Gil, R., Castro, M., & Peire, J. (2008). Mobility through location-based services at university. International Journal of Interactive Mobile Technologies (iJIM), 2(3), 34–40.Google Scholar
  42. Moodle. (2011). Retrieved 30 March, 2011, from
  43. Ogata, H., Akamatsu, R., Mitsuhara, H., Yano, Y., Matsuura, K., Kanenishi, K., et al. (2004). TANGO: Supporting vocabulary learning with RFID tags. Electronic proceedings of International Workshop Series on RFID. Tokyo.Google Scholar
  44. Özpolat, E., & Akar, G. B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53(2), 355–367.CrossRefGoogle Scholar
  45. Paredes, P., & Rodríguez, P. (2004). A mixed approach to modelling learning styles in adaptive educational hypermedia. Advanced Technology for Learning, 1(4), 210–215.CrossRefGoogle Scholar
  46. Popescu, E. (2010). Adaptation provisioning with respect to learning styles in a web-based educational system: An experimental study. Journal of Computer Assisted Learning, 26(4), 243–257.CrossRefGoogle Scholar
  47. Sakai. (2011). Retrieved 30 March, 2011, from
  48. Spada, D., Sánchez-Montañés, M., Paredes, P., & Carro, R. M. (2008). Towards inferring sequential-global dimension of learning styles from mouse movement patterns. Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture notes in computer science (Vol. 5149, pp. 337–340). Berlin: Springer.Google Scholar
  49. Tseng, J. C. R., Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51(2), 776–786.CrossRefGoogle Scholar
  50. *Woolf, B. P., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognising and responding to student affect. International Journal of Learning Technology, 4(3/4), 129–164.Google Scholar
  51. Woolf, B. P., Shute, V., VanLehn, K., Burleson, W., King, J. L., Suthers, D., et al. (2010). A roadmap for education technology. Accessed June 24, 2012, from
  52. Yang, Y. J., & Wu, C. (2009). An attribute-based ant colony system for adaptive learning object recommendation. Expert Systems with Applications, 26(2), 3034–3047.CrossRefGoogle Scholar
  53. Yin, C., Ogata, H., & Yano, Y. (2004). JAPELAS: Supporting Japanese polite expressions learning using PDA(s) towards ubiquitous learning. Journal of Information Systems Education, 3(1), 33–39.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computing and Information SystemsAthabasca UniversityEdmontonCanada
  2. 2.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada

Personalised recommendations