User Modeling and User-Adapted Interaction

, Volume 16, Issue 3–4, pp 377–401 | Cite as

The impact of learning styles on student grouping for collaborative learning: a case study

  • Enrique Alfonseca
  • Rosa M. CarroEmail author
  • Estefanía Martín
  • Alvaro Ortigosa
  • Pedro Paredes
Original Paper


Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.


Learning styles Group formation User modeling adaptation CSCL 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aroyo, L., Mizoguchi, R., Tzoloc, C.: OntoAIMS: Ontological approach to courseware authoring. In: International Conference on Computers in Education. ICCE2003. pp. 1011–1014. Wanchai, Hong Kong (2003)Google Scholar
  2. Barros, B., Verdejo, M.F.: Designing workspaces to support collaborative learning. In: IEA/AIE, Vol. 2. pp. 668–677. Castellon, Spain (1998)Google Scholar
  3. Barros, B., Verdejo, M.F.: DEGREE: un sistema para la realización y evaluación de experiencias de aprendizaje colaborativo en enseñanza a distancia. Revista Iberoamericana de Inteligencia Artificial 9, 27–37 (2000)Google Scholar
  4. Benkiran M.A., Ajhoun R.M. (2002): An adaptive and cooperative telelearning system: SMART-Learning. Int. J. on E-learning 1(2): 66–72Google Scholar
  5. Briggs K.C., Myers I.B. (1977): Myers-Briggs Type Indicator. Consulting Psychologist Press Inc, Palo, Alto CAGoogle Scholar
  6. Brown E., Cristea A., Stewart C., Brailsford T. (2005): Patterns in authoring of adaptive educational hypermedia: a taxonomy of learning styles. Educational Technology & Society 8(3): 77–90Google Scholar
  7. Brown, E.J., Brailsford, T.: Integration of learning style theory in an adaptive educational hypermedia (AEH) system. In: ALT-C Conference 2004. Exeter, UK (2004)Google Scholar
  8. Brown J.S., Collins A., Duguid P. (1989): Situated cognition and the culture of learning. Educational Researcher 18(1): 32–42CrossRefGoogle Scholar
  9. Bruner J. (1966): Toward a Theory of Instruction. Cambridge, MA, Harvard University PressGoogle Scholar
  10. Brusilovsky P. (2001): Adaptive hypermedia. User Modeling and User-Adapted Interaction. The Journal of Personalization Research 11(1&2): 87–110zbMATHGoogle Scholar
  11. Brusilovsky P., Maybury M.T. (2002): From adaptive hypermedia to the adaptive web. Communications of the ACM, 45(5): 30–33CrossRefGoogle Scholar
  12. Brusilovsky, P., Schwarz, E., Weber, G.: ELM-ART: an intelligent tutoring system on World Wide Web. In: Intelligent Tutoring Systems (ITS’96). Vol. 1086, pp. 261–269. Berlin: Springer Verlag, (1996a)Google Scholar
  13. Brusilovsky, P., Schwarz, E., Weber, G.: A tool for developing adaptive electronic textbooks on WWW. In: Proceedings of WebNet’96, World Conference of the Web Society. pp. 64–69. San Francisco, CA (1996b)Google Scholar
  14. Carro, R.M., Ortigosa, A., Martin, E., Schlichter, J.: Dynamic generation of adaptive web-based collaborative courses. In: Groupware: Design, Implementation and Use. Vol. 2806, pp. 191–198. Berlin, Heidelberg: LNCS (2003a)Google Scholar
  15. Carro, R.M., Ortigosa, A., Schlichter, J.: A Rule-based formalism for describing collaborative adaptive courses. In: Knowledge-Based Intelligent Information and Engineering Systems. Vol. 2774, pp. 252–259. Berlin, Heidelberg: LNAI (2003b)Google Scholar
  16. Carro, R.M., Pulido, E., Rodíguez, P.: Designing adaptive web-based courses with TANGOW. Advanced Research in Computers and Communications in Education 2, 697–704 (1999a)Google Scholar
  17. Carro R.M., Pulido E., Rodíguez P. (1999b): Dynamic generation of adaptive internet-based courses. J. Network Computer Appl 22, 249–257CrossRefGoogle Scholar
  18. Carro, R.M., Pulido, E., Rodíguez, P.: Developing and accessing adaptive internet-based courses. In: Virtual Environments for Teaching and Learning. pp. 111–149. World Scientific Publishing Company (2002)Google Scholar
  19. Carver C.A., Howard R.A., Lane W.D. (1999): Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Trans. Edu. 42(1): 33–38CrossRefGoogle Scholar
  20. Cassidy S. (2004): Learning styles: an overview of theories, models and measures. Educ. Psychol. 24(4): 419–444MathSciNetCrossRefGoogle Scholar
  21. Cheng, R., Vassileva, J.: Design and evaluation of an adaptive incentive mechanism for sustained educational online communities In this issue/DOI:10.1007/s11257-006-9013-6 (2006)Google Scholar
  22. Coffield, F.J., Moseley, D.V., Hall, E., Ecclestone, K.: Learning styles for post 16 learners: what do we know?. Technical report, Learning and Skills Research Centre/University of Newcastle upon Tyne, London (2004)Google Scholar
  23. Conlan, O., Dagger, D., Wade, V.: Towards a standards-based approach to e-learning personalization using reusable learning objects. In: Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education, E-Learn 2002 pp. 210–217. Montreal, Canada (2002)Google Scholar
  24. Dagger D., Wade V., Conlan O. (2005): Personalisation for all: making adaptive course composition easy. Edu. Technol. & Soci. 3, 9–25Google Scholar
  25. De Bello, T.C.: Comparison of eleven major learning style models: variables, appropriate populations, validity of instrumentation and the research behind them. J. Reading, Writing Learning Disabilities 6, 203–222 (1990)Google Scholar
  26. De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., Smits, D., Stash, N.: AHA! The adaptive hypermedia architecture. In: Paper presented at the Fourteenth ACM Conference on Hypertext and Hypermedia (HT03) (AH Workshop). pp. 81–84. Nottingham, UK (2003)Google Scholar
  27. De Bra, P., Aerts, A., Smits, D., Stash, N.: AHA! Version 2.0, more adaptation flexibility for authors. In: World Conference on e-Learning in Corporate, Government, Healthcare & Higher Education (ELearn’ 2002). pp. 240–246. Montreal, Canada (2002)Google Scholar
  28. Deibel, K.: Team formation methods for increasing interaction during in-class group work. In: Annual Joint Conference Integrating Technology into Computer Science Education. Proceedings of the 10th annual SIGCSE Conference on Innovation and Technology in Computer Science Education, pp. 291–295. Caparica, Portugal (2005)Google Scholar
  29. Dillenbourg P. (1999): Collaborative Learning: Cognitive an Computational Approaches. Oxford, UK ElsevierGoogle Scholar
  30. Dolog, P., Henze, N., Nejdl, W., Sintek, M.: The personal reader: personalizing and enriching learning resources using semantic web technologies. In: Third International Adaptive Hypermedia and Adaptive Web-based Systems Conference (AH2004), pp. 85–94. Eindhoven, The Netherlands (2004)Google Scholar
  31. Dunn R., Dunn K. (1978): Teaching Students Through Their Individual Learning Styles: A Practical Approach. Virginia, Reston PublishingGoogle Scholar
  32. Engeström Y. (1987): Learning by Expanding: An activity-Theoretical Approach to Development Research. Helsinki, Orienta-Konsultit OyGoogle Scholar
  33. Felder R.M. (1996): Matters of Style. ASEE Prism 6(4): 18–23Google Scholar
  34. Felder R.M., Silverman L.K. (1988): Learning styles and teaching styles in engineering education. Eng. Edu 78(7): 674–681Google Scholar
  35. Felder, R.M., Soloman, B.A.: Index of Learning Styles. (2004) Scholar
  36. Fröschl, C.: User Modeling and User Profiling in Adaptive E-learning Systems. Graz, Austria: Master Thesis (2005)Google Scholar
  37. Furugori, N., Sato, H., Ogata, H., Ochi, Y., Yano, Y.: COALE: Collaborative and Adaptive Learning Environment. In: CSCL 2002. pp. 493–494. Boulder, CO, USA (2002)Google Scholar
  38. Gaudioso, E., Boticario, J.G.: Supporting personalization in virtual communities in distance education. In: Virtual Environments for Teaching and Learning. World Scientific Publishing Company Pte Ltd (2002)Google Scholar
  39. Gilbert J.E., Han C.Y. (1999): Adapting instruction in search of a significant difference. J. Network Computer Applications 22, 149–160CrossRefGoogle Scholar
  40. Gottdenker, J.S., Remidez, H., Hong, R., Yoon, S., Amelung, C., Musser, D.R., Laffey, J.M.: Introduction to the shadow networkspace. In: CSCL 2002. pp. 527–528. Boulder, CO, USA (2002)Google Scholar
  41. Grigoriadou, M., Papanikolaou, K., Kornilakis, H., Magoulas, G.: INSPIRE: an intelligent system for personalized instruction in a remote environment. In: Proceedings of 3rd Workshop on Adaptive Hypertext and Hypermedia. pp. 13–24. Sonthofen, Germany (2001)Google Scholar
  42. Henze N., Naceur K., Nejdl W., Wolpers M. (1999): Adaptive hyperbooks for constructivist teaching. Kunstliche Intelligenz 4, 26–31Google Scholar
  43. Herrmann N. (1990): The Creative Brain. Lake Lure NC, Brain BooksGoogle Scholar
  44. Hockemeyer, C., Held, T., Albert, D.: RATH – a relational adaptive tutoring hypertext WWWenvironment based on knowledge space theory. In Alvegard, C.(ed.). In: Proceedings of CALISCE’98, 4th International conference on Computer Aided Learning and Instruction in Science and Engineering, pp. 417–423. Göteborg, Sweden (1998)Google Scholar
  45. Honey P., Mumford A. (1992): The Manual of Learning Styles. Peter Honey Publications, MaidenheadGoogle Scholar
  46. Hong, H., Kinshuk, D.: Adaptation to student learning styles in web based educational systems. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 491–496. Lugano, Switzerland (2004)Google Scholar
  47. Johnson, D.W., Johnson, F.P.: Learning Together: Group Theory and Group Skills. Pearson Education, (1975)Google Scholar
  48. Johnson D.W., Johnson R.T., Holubec E.J. (1984): Cooperation in the Classroom. Interaction Book Company, Edina, MNGoogle Scholar
  49. Jung C.G. (1976): Psychological Types. Princeton University Press Bollingen Series, New JerseyGoogle Scholar
  50. Kay, J.: Stereotypes, student models and scrutability. In: Cauthier, C., Frasson, G., Van (eds.): Intelligent Tutoring Systems: 5th International Conference ITS 2000, LNCS, Vol. 1839, pp. 19–30. Motreal, Canada: Springer (2000)Google Scholar
  51. Kobsa A. (2001): Generic user modeling systems. User Modeling and User-Adapted Interaction: The J. Personalization Res. 11, 49–63zbMATHCrossRefGoogle Scholar
  52. Koch, N.: Software engineering for adaptive hypermedia systems. PhD thesis. Munich, Germany: Ludwig-Maximilians-University (2000)Google Scholar
  53. Kolb D. (1984): Experiential learning: Experience as the source of learning and development. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  54. Kolb, D.A.: The Kolb Learning Style Inventory. Boston: Hay Resources Direct (1999)Google Scholar
  55. Koschmann, T.: Paradigms shift and instructional technology. In: Koschmann, T. (ed.): CSCL: Theory and practice of an emerging paradigm. pp. 1–23. New Jersey, USA: Lawrence Erlbaum Associates (1996)Google Scholar
  56. Kuutti, K., Arvonen, T.: Identify potential CSCW applications by means of activity theory concepts: a case example. In: Proceedings of the 1992 ACM conference on Computer-Supported Cooperative Work. pp. 233–240. Toronto, Ontario, Canada (1992)Google Scholar
  57. Laroussi, M., Benahmed, M.: Providing an adaptive learning through the Web case of CAMELEON: computer aided medium for learning on networks. In: Alvegard, C., (ed.): Proceedings of CALISCE’98, 4th International conference on Computer Aided Learning and Instruction in Science and Engineering. pp. 411–416. Göteborg, Sweden (1998)Google Scholar
  58. Li X., Ji Q. (2005): Active afective state detection and assistance with dynamic bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics: Special Issue on Ambient Intelligence 35(1): 93–105MathSciNetGoogle Scholar
  59. Livesay, G.A., Dee, E.A., Hites, L.S.: Engineering student learning styles: a statistical analysis using Felder’s index of learning styles. In: 2002 Annual Conference of the American Society for Engineering Education. pp. –. Montreal, Quebec (2002)Google Scholar
  60. Martín, E., Paredes, P.: Using learning styles for dynamic group formation in adaptive collaborative hypermedia systems. In: Matera, M., Comai, S., (eds.): Engineering Advanced Web Applications. Proceedings of Workshops in Connection with 4th International Conference on Web Engineering (ICWE 2004). pp. 188–197. Munich, Germany: Rinton Press, Inc (2004)Google Scholar
  61. Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. In this issue/DOI:10.1007/s11257-006-9008-3 (2006)Google Scholar
  62. McLaren, B., Walker, E., Harrer, A., Bollen, L., Sewall, J.: Creating Cognitive Tutors for Collaborative Learning: Steps Toward Realization. In this issue/DOI:10.1007/s11257-006-9007-4 (2006)Google Scholar
  63. Muehlenbrock M. (2006): Learning group formation based on learner profile and context. Int. J. E-Learning IJEL 5(1): 19–24Google Scholar
  64. Murray, T., Condit, C., Haugsjaa, E.: MetaLinks: a preliminary framework for concept-based adaptive hypermedia. In: Proceedings of Workshop ’WWW-Based Tutoring’ at 4th International Conference on Intelligent Tutoring Systems (ITS’98). San Antonio, TX (1998)Google Scholar
  65. Negro A., Scarano V., Simari R. (1998): User adaptivity on WWW through CHEOPS Tu/e Comput. Sci. Rep. 9812, 57–62Google Scholar
  66. Ortigosa, A., Carro, R.M.: The continuous empirical evaluation approach: evaluating adaptive web-based courses. In: Brusilovsky, P., Corbett, A., de Rosis F., (eds.): User Modeling 2003. LNCS 2702. pp. 163–167. Berlin, Heildelberg: Springer-Verlag (2003)Google Scholar
  67. Panitz T. (1999): The motivational benefits of cooperative learning. New dir. teach. learn. 78, 59–67CrossRefGoogle Scholar
  68. Paredes, P., Rodríguez, P.: Considering learning styles in adaptive web-based education. In: Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics, Vol. 2. pp. 481–485. Orlando, Florida (2002a)Google Scholar
  69. Paredes, P., Rodríguez, P.: Considering sensing-intuitive dimension to exposition-exemplification in adaptive sequencing. In: de Bra, P., Brusilovsky, P., Conejo, R. (eds.): Adaptive Hypermedia and Adaptive Web-Based Systems. LNCS 2347. pp. 556–559. London, UK: Springer-Verlag (2002b)Google Scholar
  70. Paredes P., Rodríguez P. (2004): A mixed approach to modelling learning styles in adaptive educational hypermedia. Adv. Technol. Learn., ACTA PRESS 1(4): 210–215Google Scholar
  71. Pilar da Silva D., Durm R.V., Duval E., Olivié H. (1998): Concepts and documents for adaptive educational hypermedia: a Model and a Prototype. Tu/e Comput. Sci. Rep. 9812, 35–43Google Scholar
  72. Rayner S., Riding R. (1997): Towards a categorisation of cognitive styles and learning styles. Educational Psychol 7(1–2): 5–27Google Scholar
  73. Read, T., Barros, B., Barcna, E., Pancorbo, J.: Coalescing Individual and Collaborative Learning to Model User Linguistic Competences. In this issue/DOI:10.1007/s11257-006-9014-5 (2006)Google Scholar
  74. Schlichter J. (1997): Lecture 2000: More than a course across wires. Teleconference – The Business Communications Magazine 16(6): 18–21Google Scholar
  75. Schöch, V., Specht, M., Weber, G.: ADI – an empirical evaluation of a tutorial agent. In: Proceedings of ED-MEDIA/ED-TELECOM’98 - 10th World Conference on Educational Multimedia and Hypermedia and World Conference on Educational Telecommunications. pp. 1242–1247. Freiburg, Germany (1998)Google Scholar
  76. Seeberg, C., Rechenberger, K., Fischer, S., Steinmetz, R., Steinacker, A.: Dynamically generated tables of contents as guided tours in adaptive hypermedia systems. In: Kommers, P., Richards, G., (eds.): Proceedings of 11th World Conference on Educational Multimedia, Hypermedia and Telecommunications. pp. 640–645. Chesapeake, VA (1999)Google Scholar
  77. Seery, N., Gaughran, W.F., Waldmann, T.: Multi-modal learning in engineering education. In: Proceedings of the ASEE Conference on Engineering Education. Nashville (2003)Google Scholar
  78. Self, J.: Formal approaches to student modelling. In: Student Modelling: the Key to Individualize Knowledge-Based Instruction. pp. 295–352. Berlin: Springer-Verlag (1994)Google Scholar
  79. Slavin R.E. (1980): Cooperative learning. Rev. Educ. Res. 50(2): 315–342CrossRefGoogle Scholar
  80. Soller A. (2001): Supporting social interaction in an intelligent collaborative learning system. Int. J. Artif. Intell. Edu. 12(1): 40–62Google Scholar
  81. Specht M., Oppermann R. (1998): ACE—adaptive courseware environment. The New Rev. Hypermedia Multimedia, 4, 141–162Google Scholar
  82. Specht, M., Weber, G., Heitmeyer, S., Schöch, V.: AST: adaptive WWW-courseware for statistics. In: Brusilovsky, P., Fink, J., Kay, J. (eds.): Proceedings of Workshop Adaptive Systems and User Modeling on the World Wide Web at 6th International Conference on User Modeling, UM97. pp. 91–95. Chia Laguna, Sardinia, Italy (1997)Google Scholar
  83. Stash, N., Cristea, A., De Bra, P.: Authoring of learning styles in adaptive hypermedia: problems and solutions. In: World Wide Web Conference 2004. pp. 114–123. New York, USA (2004)Google Scholar
  84. Stern, M., Woolf, P.: Adaptive content in an online lecture system. In: Adaptive Hypermedia and Adaptive Web-Based Systems: International Conference, AH 2000, Vol. 1892 of Lecture Notes in Computer Science. pp. 227–238. Springer Berlin / Heidelberg (2000)Google Scholar
  85. Stoyanov S., Kirschner P. (2004): Expert concept mapping method for defining the characteristics of adaptive e-learning: ALFANET project case. Edu. Technol. Res. Dev. 52(2): 41–56CrossRefGoogle Scholar
  86. Suebnukarn, S., Haddawy, P.: Modeling individual and collaborative problem-solving in medical problem-based learning. In this issue/DOI:10.1007/s11257-006-9011-8 (2006)Google Scholar
  87. Suthers, D., Xu, J.: Kükäkükä: an online environment for artifact-centered discourse. In: Proceedings of the Education Track of the Eleventh World Wide Web Conference. pp. 472–480. Honolulu, Hawaii (2002)Google Scholar
  88. Swanson, L.J.: Learning styles: a review of the literature. Document no. ed 387 067, Educational Research Information Centre (ERIC) (1995)Google Scholar
  89. Triantafillou, E., Pomportsis, A., Georgiadou, E.: AESCS: adaptive educational system base on cognitive styles. In: Brusilovsky, P., Henze, N., Millán, E. (eds.): Proceedings of the AH’2002 Workshop on Adaptive Systems for Web-based Education. pp. 1–12. Málaga, Spain (2002)Google Scholar
  90. Vassileva, J.: Dynamic courseware generation on the WWW. In: d. Boulay, B., Mizoguchi, R. (eds.): Artificial Intelligence in Education: Knowledge and Media in Learning Systems. Proceedings of AI-ED’97, 8th World Conference on Artificial Intelligence in Education. pp. 498–505. Amsterdam: IOS (1997)Google Scholar
  91. Vygotsky L.S. (1978): Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA, Harvard University PressGoogle Scholar
  92. Webb G., Pazzani M.J., Billsus D. (2001): Machine learning for user modeling. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 11(1–2): 19–29zbMATHCrossRefGoogle Scholar
  93. Wessner, M., Pfister, H.: Group formation in computer-support collaborative learning. In: Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work, ACM Press. pp. 24–31. NY, USA (2001)Google Scholar
  94. Witkin H.A., Goodenough D.R. (1981): Cognitive Styles, Essence and Origins: Field Dependence and Field Independence. New York, International Universities PressGoogle Scholar
  95. Wolf, C.: iWeaver: towards an interactive web-based adaptive learning environment to address individual learning styles. Euro. J. Open Distance E-learn. (EURODL 2002) (2002)Google Scholar
  96. Zywno, M.S.: A contribution of validation of score meaning for felder-soloman’s index of learning styles. In: Proceedings of the 2003 Annual ASEE Conference, Washington, DC: American Society for Engineering Education. Nashville, Tennessee (2003)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Enrique Alfonseca
    • 1
  • Rosa M. Carro
    • 1
    Email author
  • Estefanía Martín
    • 1
  • Alvaro Ortigosa
    • 1
  • Pedro Paredes
    • 1
  1. 1.Computer Science DepartmentUniversidad Autonoma de MadridMadridSpain

Personalised recommendations