Toward Personalised Gamification for Learning Environments

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Many learning environments are deserted by the learners, even if they are effective. Gamification is a growing approach used to raise learners’ motiva-tion by adding game elements in their environment, but it still pays few attention to the individual differences among learners’ motivations. This paper presents a gamification system designed to be plugged on various learning environments. It can be automatically personalised, based on an analysis of the interaction traces.

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Toward Personalised Gamification for Learning Environments

  1. 1. Toward Personalised Gamification for Learning Environments Baptiste Monterrat1 Élise Lavoué1 Sébastien George2 MATEL workshop 2013, Paphos, Cyprus 1 LIRIS, Université de Lyon 2 LIUM, Université du Maine
  2. 2. Learning environments, even if they are efficient, are not always motivating 2B. Monterrat - Université de Lyon
  3. 3. Game and “fun” enhance motivation Learning environments, even if they are efficient, are not always motivating 3B. Monterrat - Université de Lyon
  4. 4. Gamification Serious Games Toys Playful design • A game has an objective and rules Game Play 4B. Monterrat - Université de Lyon Forms of “fun” in learning [Deterding et al., 2011]
  5. 5. Gamification Serious Games • A game has an objective and rules Game Play 5B. Monterrat - Université de Lyon Forms of “fun” in learning [Deterding et al., 2011]
  6. 6. Gamification “use of game design elements in non-gaming contexts” [Deterding et al., 2011] Gamification Serious Games • Game elements are less central with gamification • Gamification can be based on existing environments Whole Part 6B. Monterrat - Université de Lyon
  7. 7. Various expectations about games Killer Socialiser Achiever Explorer Motivated by leader boards Motivated by friends Motivated by clear goals Motivated by discoveries • According to player types [Bartle, 1996] 7B. Monterrat - Université de Lyon
  8. 8. Various expectations about games Killer Socialiser Achiever Explorer Motivated by leader boards Motivated by friends Motivated by clear goals Motivated by discoveries • According to player types [Bartle, 1996] • According to user’s age. [Charlier et al., 2012] 8B. Monterrat - Université de Lyon
  9. 9. Various expectations about games Killer Socialiser Achiever Explorer Motivated by leader boards Motivated by friends Motivated by clear goals Motivated by discoveries • According to player types [Bartle, 1996] • According to user’s age. [Charlier et al., 2012] • According to user’s gender. [Hainey et al., 2012] • Etc. 9B. Monterrat - Université de Lyon
  10. 10. Adaptivity 10B. Monterrat - Université de Lyon
  11. 11. Adaptivity • Work on adaptive learning games : • Content • Scenario • Difficulty • Learning path • Etc. • Work on adaptive gamification: • Not yet 11B. Monterrat - Université de Lyon
  12. 12. Our objective Adaptive and generic Gamification layer Web based Learning Environment 12B. Monterrat - Université de Lyon
  13. 13. Research questions Which architecture to support adaptivity of game elements in a learning environment? How to characterise game elements? User Model : Which information is required for personalisation? How to integrate the game elements in the learning environment? 13B. Monterrat - Université de Lyon
  14. 14. Game Elements in Epiphytic Functionalities Four features to be an epiphyte: [Giroux et al., 1995] • The epiphyte can not exist without its host • The host can exist without the epiphyte • The architecture of the epiphyte is independent from the host architecture • The epiphyte does not affect its host 14B. Monterrat - Université de Lyon
  15. 15. User Model: Related work in adaptive environments • Kobsa (1999) distinguishes three forms of adaptation: user data, usage data, environment data • Conati (2002) focuses on what the learner knows and does not know. • Bernardini (2010) classifies the learners according to their way of learning High Learner, Low Learner • Bartle’s player types (1996) Killer, Achiever, Socialiser, Explorer • Lazzaro’s keys of fun (2004) Hard fun, Easy fun, Altered state, People factor • Yee’s motivation components (2006) Achievement, Social, Immersion The user as a learner User model elements for personalisation of learning environments The user as a player 15B. Monterrat - Université de Lyon
  16. 16. 16 Gamification layerLearning Environment The User Model for Adaptive Gamification Learner Model Player Model B. Monterrat - Université de Lyon
  17. 17. 17 Gamification layerLearning Environment The User Model for Adaptive Gamification Learner Model Player Model Collected data Calculated data • Player type • Engagement level B. Monterrat - Université de Lyon
  18. 18. I – USER DATA • Age • Gender • Location 18 Gamification layerLearning Environment The User Model for Adaptive Gamification Learner Model Player Model Collected data Calculated data III - ENVIRONMENT DATA • Learning context • Size of the group • Date, hour • Device II - USAGE DATA • Session dates • Usage of epiphytes • Response time • Level of success • Player type • Engagement level B. Monterrat - Université de Lyon
  19. 19. 1. Collect the trace of interactions between the learner and environment, * 2. Detect learner disengagement, * 3. Choose a gamified functionality to activate, 4. Integrate the gamified functionality within the UI. * 19 * User model updates Gamification Process B. Monterrat - Université de Lyon
  20. 20. Architecture 20B. Monterrat - Université de Lyon
  21. 21. Conclusion and Future works We explained the interest for gamification to be adaptive and generic, and proposed a user model and an architecture to support such gamification. 21B. Monterrat - Université de Lyon
  22. 22. Conclusion and Future works We explained the interest for gamification to be adaptive and generic, and proposed a user model and an architecture to support such gamification. We are working on the first implementation of this system, based on an environment to learn spelling. Development and assessments will be iterative. 22B. Monterrat - Université de Lyon
  23. 23. Conclusion and Future works We explained the interest for gamification to be adaptive and generic, and proposed a user model and an architecture to support such gamification. We are working on the first implementation of this system, based on an environment to learn spelling. Development and assessments will be iterative. We hope that this work is a step toward more motivating learning environments. 23B. Monterrat - Université de Lyon
  24. 24. B. Monterrat - Université de Lyon 24 Thanks for your attention baptiste.monterrat@universite-lyon.fr
  25. 25. Keep in mind… Game should remain a voluntary activity. 25 We can’t turn everything into a game. Games should not replace learning. Personalisation to user data has limits. B. Monterrat - Université de Lyon
  26. 26. Integration of the epiphytic functionalities 26 Popup window Tooltip Footer Side Panel B. Monterrat - Université de Lyon

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