Personalized, Adaptive Digital Educational Games Using Narrative Game-Based Learning Objects

Göbel, Stefan; Wendel, Viktor; Ritter, Christopher; Steinmetz, Ralf

doi:10.1007/978-3-642-14533-9_45

Cited by 39 publications

(20 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During the second phase, the player profile was used to predict which story events will be more relevant for the player. Göbel, Wendel, Ritter, and Steinmetz (2010) used a similar approach to adapt the learning contents, the game elements and the scenario of a learning game. Their model was based on Bartle's player types.…”

Section: Relating Gaming Features and The Player Modelmentioning

confidence: 99%

“…We proposed a player model based on the BrainHex typology as well as adaptation rules and formulae for selecting the gaming features most adapted to the learners' player profile. Considering the player model, the majority of existing works on adaptation in games are based on Bartle's typology (Challco, Moreira, Mizoguchi, & Isotani, 2014;Gil, Cantador, & Marczewski, 2015;Göbel et al, 2010) although this typology is specific to MMORPG. As gamification can be applied to any non-game contexts (Deterding et al, 2011), the use of more generic classifications is required.…”

Section: Implications For Practicementioning

confidence: 99%

See 1 more Smart Citation

Adaptation of Gaming Features for Motivating Learners

Monterrat

Lavoué

George

2017

Simulation & Gaming

View full text Add to dashboard Cite

Background. Many learning environments are quickly deserted by learners, even if they are efficient. Gamification of learning environments is a recent approach used to enhance learners' motivation and participation. Aim. One issue with this approach is that people have various expectations and react differently faced with specific game mechanics. So, an important goal lies in automatically adapting game mechanics according to player types. In this paper, we study the gaming features that can be adapted in learning environments and the player model that can be used for the adaptation process. We propose an approach that aims to predict to which game mechanics a user is responsive, and to adapt the gaming features of the system according to this information. Methodology. An implementation was released, and evaluated through an exploratory study with 59 middle school students, each one using the learning environment during three 45minutes sessions. Results. The results validate the implementation of the system and show that the users' activity can help to predict their profile. The adaptation process did not improve learners' engagement as expected, but it shows a path for future research toward an adaptive approach for learning environment gamification.

show abstract

Section: Relating Gaming Features and The Player Modelmentioning

confidence: 99%

Section: Implications For Practicementioning

confidence: 99%

Adaptation of Gaming Features for Motivating Learners

Monterrat

Lavoué

George

2017

Simulation & Gaming

View full text Add to dashboard Cite

show abstract

“…Some research focuses on personalisation via broad player types. For example, Göbel et al (2010) deploy a variation of Bartle's model (killer, achiever, socialiser, and explorer) which is updated as the player makes decisions, while Holmes et al (2015) use game state changes and visual, auditory or haptic player feedback resulting from player interactions with the game mechanics to help determine players as disruptors, free spirits, achievers, players, socialisers, or philanthropists and promote certain behaviour changes. Ferro et al (2013) derive relationships between player types, personality types and traits, and game elements and game mechanics.…”

Section: Personalisation In Gamesmentioning

confidence: 99%

Reflective agents for personalisation in collaborative games

2018

View full text Add to dashboard Cite

The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation ('reflective personalisation') in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.

show abstract

“…80 Days [13] is a promising research project of narrative-based educational game with adaptive behaviour. It is an ongoing project in which the player helps an alien, Feon, to write a travel guide about planet Earth.…”

Section: B Related Workmentioning

confidence: 99%

Towards Player Adaptivity in a Serious Game for Conflict Resolution

Grappiolo

Cheong

Togelius

et al. 2011

2011 Third International Conference on Games and Virtual Worlds for Serious Applications

View full text Add to dashboard Cite

Abstract-We present a technology demonstrator for an adaptive serious game for teaching conflict resolution and discuss the research questions associated with the project. The prototype is a single-player 3D mini-game which simulates a resource management conflict scenario. In order to teach the player how to resolve this type of conflict, the underlying system generates level content automatically which adapts to player experience and behaviour. Preliminary results demonstrate the efficiency of the procedural content generation mechanism in guiding the training of players towards targeted learning objectives.

show abstract

Personalized, Adaptive Digital Educational Games Using Narrative Game-Based Learning Objects

Cited by 39 publications

References 8 publications

Adaptation of Gaming Features for Motivating Learners

Adaptation of Gaming Features for Motivating Learners

Reflective agents for personalisation in collaborative games

Towards Player Adaptivity in a Serious Game for Conflict Resolution

Contact Info

Product

Resources

About