# Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study
## 6 Discussion  
The results of our study conclude that the method \(A\)--where player type ratings are fixed along the experience--is the best when the users thoroughly and accurately answer the questionnaire, while our method, method \(C\), works well in both cases (users either accurately or inaccurately respond to it). These results are aligned with other studies that considered player type ratings as fixed throughout the experience [4] (see method A of our experiments). Nevertheless, previous studies did not consider the dynamic player type method. Since it will not be possible to know whether (real) users accurately answer the player type questionnaire in real situations, our method is the most suitable for an adaptive gamified experience. Our adaptive method needs information about the player type of the user at the beginning of the experience, so we used the HEXAD player types questionnaire proposed by [20]. It is worth noting that a recent study that describes a new method to predict player types using gameful applications [23]. As we mentioned in Section 3, we selected 14 game elements of the 52 presented in [24]. In the case of extending the proposal from 14 to 52 game elements, our adaptive method is still applicable, it would only be necessary to extend the GE and GM vectors of Equations (3) and (4) and the dimensions of the matrix M, Equation (5). Moreover, our approach is easily applicable to different player types taxonomies such as MoMo [21], a new promising model created as a combination of others, which will be considered in our future research. However, our system has some limitations. Actually, it is necessary to test different values of updating the frequency of execution of player ratings (Step 1 of the method, as can be seen in Figure 2). Current simulations have used a frequency of one iteration every 15 s, ensuring that changes in the bots' behavior can be detected without overloading the system performance, but this value can be better tuned and even more so when considering real-life scenarios. Moreover, analyzing real users' experiences, we could tune some constants such as \(\epsilon\) or the values of the matrix \(M\) to better adapt the users' profile and the gamified experience. Note that in the process of measuring the error \(Err\) in the simulated scenario, the average distance between \(PR^{(t)}\) and \(RPT_{0}\) is considered, and not the distance between \(PR^{(t)}\) and \(RPT_{t}\). However, it would be possible to calculate the error with respect to a dynamic \(RPT_{t}\), in which case we would slightly randomly change some values of \(RPT_{t}\), and therefore, from this value, the behavior of the bots (\(\Theta\), \(\top\)) would be dynamically calculated. Additionally, a simulation of a bigger change of the real player profile would be possible since it consists of carrying out our simulation n times with a different \(RPT_{0}\) each time. Indeed, the error is currently calculated as the mean of the distances between \(PR^{(t)}\) and \(RPT_{0}\) of all the types of players (see Equation (12)), which may hide the particular behavior of individual player types (e.g., disruptor, free spirit). Figure 9: Evolution of the player rating in the best case in inaccurate answers (method C): philanthropist (yellow); free spirit (green); and disruptor (red). The current evaluation performed using bots should be extended to real users. Thus, additional users' experience data, such as the course and activities completion rate and users' satisfaction, could be used to analyze the effectiveness of the method. The current case study, which is a short course composed of a few activities developed during a short period of time, is somehow limited in terms of the number of collected opinions and interactions. Our method should be applied to other larger gamified systems. Additionally, our method has a limited representation of user engagement behavior using just interactions, interaction speed and opinions.