toward personalised gamification for learning environments
DESCRIPTION
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.TRANSCRIPT
Toward Personalised Gamificationfor Learning Environments
Baptiste Monterrat1
Élise Lavoué1
Sébastien George2
MATEL workshop 2013,Paphos, Cyprus
1 LIRIS, Université de Lyon 2 LIUM, Université du Maine
Learning environments, even if they are efficient, are not always motivating
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Game and “fun”enhance motivation
Learning environments, even if they are efficient, are not always motivating
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GamificationSerious Games
ToysPlayful design
• A game has an objective and rules
Game
Play
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Forms of “fun” in learning [Deterding et al., 2011]
GamificationSerious Games
• A game has an objective and rules
Game
Play
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Forms of “fun” in learning [Deterding et al., 2011]
Gamification “use of game design elements in non-gaming contexts”[Deterding et al., 2011]
GamificationSerious Games
• Game elements are less central with gamification
• Gamification can be based on existing environments
Whole Part
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Various expectations about games
Killer
Socialiser
Achiever
Explorer
Motivated byleader boards
Motivated byfriends
Motivated by clear goals
Motivated bydiscoveries
• According to player types [Bartle, 1996]
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Various expectations about games
Killer
Socialiser
Achiever
Explorer
Motivated byleader boards
Motivated byfriends
Motivated by clear goals
Motivated bydiscoveries
• According to player types [Bartle, 1996]• According to
user’s age.[Charlier et al., 2012]
8B. Monterrat - Université de Lyon
Various expectations about games
Killer
Socialiser
Achiever
Explorer
Motivated byleader boards
Motivated byfriends
Motivated by clear goals
Motivated bydiscoveries
• According to player types [Bartle, 1996]• According to
user’s age.[Charlier et al., 2012]
• According touser’s gender.[Hainey et al., 2012]
• Etc.
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Adaptivity
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Adaptivity
• Work on adaptivelearning games :• Content• Scenario• Difficulty• Learning path• Etc.
• Work on adaptive gamification:• Not yet
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Our objective
Adaptive and generic Gamification layer
Web based Learning
Environment
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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?
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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
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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 learningHigh 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
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Gamification layerLearning Environment
The User Model for Adaptive Gamification
Learner Model Player Model
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Gamification layerLearning Environment
The User Model for Adaptive Gamification
Learner Model Player Model
Collected data Calculated data
• Player type
• Engagement level
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I – USER DATA• Age• Gender• Location
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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
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. *
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* User model updates
Gamification Process
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Architecture
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Conclusion and Future works
We explained the interest for gamification to be adaptiveand generic,and proposed a user model and an architecture to support such gamification.
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Conclusion and Future works
We explained the interest for gamification to be adaptiveand 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.
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Conclusion and Future works
We explained the interest for gamification to be adaptiveand 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.
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Keep in mind…
Game should remain a voluntary activity.
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We can’t turn everything into a game.
Games should not replace learning.
Personalisation to user data has limits.
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Integration of the epiphytic functionalities
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Popup window
Tooltip
Footer
Side Panel
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