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Approaches to Detecting and Utilizing Play and Learning Styles in Adaptive Educational Games

  • Renny S. N. Lindberg
  • Teemu H. Laine
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 739)

Abstract

Games have emerged as promising tools to make learning more fun. Pedagogical effectiveness of an educational game can increase if its behavior changes according to learners’ play and learning styles. Several models for categorizing learning and play styles exist, but not many studies simultaneously detect and utilize both style groups. To alleviate this, as the first contribution, we analyzed and compared existing learning and play style models, and chose the most suitable one from each group. Personality style models were also discussed. We then created a questionnaire based on Honey and Mumford’s Learning Style Questionnaire and Bartle’s Player Types, and collected data from 127 South Korean elementary school children. The results indicated that specific play styles were clearly more dominant (Killer 18%, Achiever 24%, Explorer 32%, Socializer 41%), whereas dominant learning styles were distributed more evenly (Activist 33%, Reflector 37%, Theorist 20% and Pragmatist 25%). As the second contribution, we presented the foundations of a generic adaptation model for utilizing learning and play styles for designing adaptive educational games.

Keywords

Learning styles Play styles Personality types Educational games Adaptation Questionnaire 

References

  1. 1.
    Abadzi, H.: Efficient learning for the poor: new insights into literacy acquisition for children. Int. Rev. Educ. 54, 581–604 (2008). Washington, DC: The World Bank, Washington D.CCrossRefGoogle Scholar
  2. 2.
    Andersen, E., Downey, B.: The MUD personality test. MUD Companion 1, 33–35 (2001)Google Scholar
  3. 3.
    Arnab, S., Brown, K., Clarke, S., Dunwell, I., Lim, T., Suttie, N., Louchart, S., Hendrix, M., De Freitas, S.: The development approach of a pedagogically-driven serious game to support Relationship and Sex Education (RSE) within a classroom setting. Comput. Educ. 69, 15–30 (2013)CrossRefGoogle Scholar
  4. 4.
    Bartle, R.: Hearts, clubs, diamonds, and spades: players who suit MUDs. J. MUD Res. 1(1), 19 (1996)Google Scholar
  5. 5.
    Bateman, C., Boon, R.: 21st Century Game Design, 1st edn. Charles River Media, Hingham (2005)Google Scholar
  6. 6.
    Bateman, C., Lowenhaupt, R., Nacke, L.E.: Player typology in theory and practice. In: Proceedings of DiGRA 2011 Conference: Think Design Play, pp. 1–24 (2011)Google Scholar
  7. 7.
    Bontchev, B., Vassileva, D.: Learning objects types dependability on styles of learning. In: Proceedings of 8th WSEAS International Conference, pp. 227–234 (2011)Google Scholar
  8. 8.
    Briggs, K.C.: Myers-Briggs Type Indicator. Consulting Psychologist Press, Inc., Palo Alto (1977)Google Scholar
  9. 9.
    Canavan, J.: Personalised E-Learning through learning style aware adaptive systems. Thesis, p. 0 (2005)Google Scholar
  10. 10.
    Coenen, T., Mostmans, L., Naessens, K.: MuseUs: case study of a pervasive cultural heritage serious game. J. Comput. Cult. Herit. 6(2), 1–19 (2013)CrossRefGoogle Scholar
  11. 11.
    Coffield, F., Moseley, D., Hall, E., Ecclestone, K.: Should we be using learning styles? What research has to say to practice. Technical report, Learning and Skills Research Centre, London (2004). www.LSRC.ac.uk
  12. 12.
    Connolly, T.M., Boyle, E.A., Macarthur, E., Hainey, T., Boyle, J.M.: A systematic literature review of empirical evidence on computer games and serious games. Comput. Educ. 59(2), 661–686 (2012)CrossRefGoogle Scholar
  13. 13.
    Del Corso, D., Ovcin, E., Morrone, G., Gianesini, D., Salojarvi, S., Kvist, T.: 3DE: an environment for the development of learner-centered custom educational packages. In: Proceedings - Frontiers in Education Conference, vol. 2, pp. F2C/21–F2C/26 (2001)Google Scholar
  14. 14.
    Felder, R.M.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  15. 15.
    Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)Google Scholar
  16. 16.
    Furnham, A.: The big five versus the big four: the relationship between the Myers-Briggs Type Indicator (MBTI) and NEO-PI five factor model of personality. Pers. Individ. Differ. 21(2), 303–307 (1996)CrossRefGoogle Scholar
  17. 17.
    Gillen, K.: Retrospective: Planescape Torment (2010). https://www.rockpapershotgun.com/2010/09/28/re-retrospective-planescape-torment/
  18. 18.
    Honey, P., Mumford, A.: The Manual of Learning Styles, 3rd edn. Peter Honey Publications, Maidenhead (1982)Google Scholar
  19. 19.
    Jung, C.G.: Psychological Types: The Collected Works, vol. 6. Routledge and Kegan Paul, London (1971)Google Scholar
  20. 20.
    Ke, F., Grabowski, B.: Gameplaying for maths learning: cooperative or not? Brit. J. Educ. Technol. 38(2), 249–259 (2007)CrossRefGoogle Scholar
  21. 21.
    Keirsey, D., Bates, M.: Please Understand Me: Character and Temperament Types, 5th edn. Prometheus Nemesis Book Company, Del Mar (1984)Google Scholar
  22. 22.
    Keirsey, D., Bates, M.: Please Understand Me: Temperament, Character, Intelligence. Prometheus Nemesis Book Company, Del Mar (1998)Google Scholar
  23. 23.
    Kim, B., Park, H., Baek, Y.: Not just fun, but serious strategies: using meta-cognitive strategies in game-based learning. Comput. Educ. 52(4), 800–810 (2009)CrossRefGoogle Scholar
  24. 24.
    Kolb, A.Y., Kolb, D.A.: The Kolb Learning Style Inventory - Version 3.1 2005 Technical Specifications, vol. 200. Hay Resource Direct, Boston (2005)Google Scholar
  25. 25.
    Kolb, D.A.: Experential Learning: Experience as the Source of Learning and Development. Prentice Hall, Englewood Cliffs (1984)Google Scholar
  26. 26.
    Konert, J., Göbel, S., Steinmetz, R.: Modeling the player, learner and personality: independency of the models of Bartle, Kolb and NEO-FFI (Big5) and the implications for game based learning. In: Proceedings of the 7th European Conference on Games Based Learning, pp. 329–335 (2013)Google Scholar
  27. 27.
    Koops, M., Hoevenaar, M.: Conceptual change during a serious game: using a lemniscate model to compare strategies in a physics game. Simul. Gaming 44(4), 544–561 (2012)CrossRefGoogle Scholar
  28. 28.
    Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: INSPIRE: an INtelligent system for personalized instruction in a remote environment. In: Reich, S., Tzagarakis, M.M., Bra, P.M.E. (eds.) AH 2001. LNCS, vol. 2266, pp. 215–225. Springer, Heidelberg (2002). doi: 10.1007/3-540-45844-1_21 CrossRefGoogle Scholar
  29. 29.
    Lazzaro, N.: Why we play games: four keys to more emotion without story. In: Game Developers Conference, July 2004Google Scholar
  30. 30.
    Lindberg, R.S.N., Laine, T.H.: Detecting play and learning styles for adaptive educational games. In: 8th International Conference on Computer Supported Education, vol. 1, pp. 181–189. SCITEPRESS Science and Technology Publications (2016)Google Scholar
  31. 31.
    Macvean, A., Robertson, J.: iFitQuest: a school based study of a mobile location-aware exergame for adolescents. In: Proceedings of the 14th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI 2012, p. 359 (2012)Google Scholar
  32. 32.
    Magoulas, G., Papanikolaou, K., Grigoriadou, M.: Adaptive web-based learning: accommodating individual differences through system’s adaptation. Brit. J. Educ. Technol. 34(4), 511–527 (2003)CrossRefGoogle Scholar
  33. 33.
    McMahon, N., Wyeth, P., Johnson, D.: Personality and player types in fallout new vegas. In: Proceedings of the 4th International Conference on Fun and Games - FnG 2012, pp. 113–116 (2012)Google Scholar
  34. 34.
    Merrill, M.D.: “Component Display Theory”. Instructional-Design Theories and Models: An Overview of Their Current Status, 1st edn. Lawrence Erlbaum Associates, London (1983)Google Scholar
  35. 35.
    Mirvis, P.H.: Flow: The Psychology of Optimal Experience, vol. 16, 1st edn. Harper & Row, New York (1991)Google Scholar
  36. 36.
    Morelli, T., Foley, J., Lieberman, L.: Pet-N-Punch: upper body tactile/audio exergame to engage children with visual impairments into physical activity. Proc. Graph. Interface 2011, 223–230 (2011)Google Scholar
  37. 37.
    Myers, I., Myers, P.: Gifts Differing: Understanding Personality Type. Nicholas Brealey Publishing, London (2010)Google Scholar
  38. 38.
    Myers, I.B.: The Myers-Briggs Type Indicator: Manual (1962). Consulting Psychologists Press, Palo Alto (1962)CrossRefGoogle Scholar
  39. 39.
    Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: preliminary results from a neurobiological gamer typology survey. In: Anacleto, J.C., Fels, S., Graham, N., Kapralos, B., Saif El-Nasr, M., Stanley, K. (eds.) ICEC 2011. LNCS, vol. 6972, pp. 288–293. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24500-8_31 CrossRefGoogle Scholar
  40. 40.
    Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: a neurobiological gamer typology survey. Entertain. Comput. 5(1), 55–62 (2014)CrossRefGoogle Scholar
  41. 41.
    Nygren, E., Sutinen, E., Blignaut, A.S., Laine, T.H., Els, C.J.: Motivations for play in the UFractions mobile game in three countries. Int. J. Mob. Blended Learn. 4(2), 30–48 (2012)CrossRefGoogle Scholar
  42. 42.
    Oliver, J., Sanjay, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. Handb. Pers. Theor. Res. 2, 102–138 (1999)Google Scholar
  43. 43.
    Orji, R., Vassileva, J., Mandryk, R.L.: Modeling the efficacy of persuasive strategies for different gamer types in serious games for health. User Model. User Adap. Interact. 24(5), 453–498 (2014)CrossRefGoogle Scholar
  44. 44.
    Popescu, M.M., Romero, M., Usart, M., National, C.I.: Using serious games in adult education serious business for serious people-the metavals game case study. Learning 2(1), 68–72 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceVrije Universiteit BrusselBrusselsBelgium
  2. 2.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologySkellefteåSweden

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