# Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study
## 2 Previous Work
### Adaptation Strategy  
Recent literature reviews have analyzed adaptive gamification in educational and collaborative systems [8; 27]. In the educational context, different studies linked game elements to students' motivation and their player and learner type [11; 13; 28; 29]. These studies laid the groundwork on which adaptation studies were constructed upon, which brought a variety of contributions from adaptation engine architectures [16; 30] to evaluating gamification effectiveness [4; 31; 32]. The main needs of adaptive gamification systems emerging from the literature analysis in education are to enhance learners' models, to explore different adaptation methods, especially dynamic adaptation, and to assess the long-term impact of gamification in learner performance or motivation.  
Furthermore, gameful applications for motivating ones; participation in collaborative systems spread between serious games [33; 34] and gamified experiences [35]. There were different approaches to adaptation in gamified collaborative systems. These include difficulty adaptation, which that either be based on the player behavior (player's performance) [36; 37] or based on the global behavior of a group of players [38], adaptive curriculum guidance [39; 40], storytelling and content adaptation [41; 42], adaptive presentation [43], and motivational interventions [32; 44]. Our research work relates to the last two approaches as it presents to the user those game elements that fit their profile to motivate them to complete the course. Moreover, [PERSON] et al. [27] formalized adaptation strategies and proposed a new taxonomy--gamification elements adaptation strategy (GEAS)--for the adaptation of gamification elements. The so-called full GEAS strategy refers to the adaptation that applies different gamification elements at different moments depending on the estimated user preferences [40; 45]. Single GEAS adjusts some features of the gamification elements according to players' behavior [32; 46]. Our research situates in full the GEAS element of the taxonomy as our adaptation provides different game elements depending on the MOOC's learners' profile and their behavior along the course.  
Nevertheless, there is still room for improvement regarding dynamic adaptation, where we find few studies. [PERSON] [4] proposed a matrix factorization model similar to those used in recommender systems. They used two matrices: one defining the player types of all users and the other representing how the game elements match the player types. They combined these two matrices to obtain game elements' scores for each user, and then they selected the element with the highest score. Another study [15] defined an off-line Q-Learning algorithm to generate an adaptive learning path for the user. The authors specified an S-Table and a Q-Table that represented the corresponding state at each taken action, and the Q-values of each action in each state, respectively. Both tables were the same for all the profiles. Nevertheless, the R-Table (the reward of each action in each state) was specific for each learning-player profile. All of them considered adapting game elements to the initial player's profile, keeping the player type static throughout the experience.  
Our adaptive algorithm is based on a matrix factorization model similar to [4], which allows the recalculation of the player type, i.e., of all player ratings, during the experience in order to adapt the game element to the user model at any given moment. In this initial study, adaptation is based on activating one of the most appropriate game elements at any time depending on the recalculated player type.