# Evaluating Learner Engagement with Gamification in Online Courses
## 3 Static Adaptive Gamification versus Dynamic Adaptive Gamification  
In this section, we describe two Adaptive Gamification strategies, considering that diverse groups of users have differing motivations during an ongoing MOOC. These adaptive strategies are based on the HEXAD player types classification [44], which will guide the dynamic selection of the game elements to be shown during the MOOC. Adaptive Gamification strategies use player types to guide the selection of the game elements that will appear to the students during the MOOC, in contrast to \"one-fits-all\" strategies that consider that all the students have the same profile. One of the first approaches to adaptive gamification was proposed by [PERSON] et al. [15], who obtained a player profile at the beginning of the experiment, assuming that this player profile remained constant (static) over time. They relied on the BrainHex typology ([31]) which represents the player profile as a vector of values of player types, so several player types were considered, and not only the predominant one [45]. Moreover, they proposed a matrix factorization model to obtain the most suitable gamification element to be shown. They used two matrices, the first of which contains the player types of all the users, while the second matches the game elements and the player types (i.e., the Matching Matrix). The combination of these two matrices yield the game element scores for each user. Finally, the algorithm selects and shows the element with the highest score to the user. We use the term Static Adaptive Gamification strategy, _StaticAG_, to refer to a modified version of [PERSON]'s [15] original idea. In this static version [16], we keep her main idea to initially obtain a player profile and maintain it as constant throughout the experience. However, we used the HEXAD classification of player types ([32]), which is a slightly different classification compared to BrainHex, as used by them. While BrainHex is a classification oriented towards video game players, HEXAD focuses more on the psychological aspects of gamified virtual environments. We also consider the case of participants that do not want to play (non-player type). Then, in the _StaticAG_ version, we define the Player Profile vector as a 7-length array containing the HEXAD classification player types plus a non-player type. Consequently, we added the set of game elements related to these new player types--which is a subset of those proposed by [PERSON] [44]. In Figure 1, the Player Profile is shown as a 7-length vector, where each component represents the ratings corresponding to each player type in the range \([0,1]\). For example, a HEXAD Individual Player Profile defined as (0.25, 0.15, 0.2, 0.4, 0.4, 0.1, 0) means that the student is 25% disruptor, 15% free spirit, 20%achieved, 40% player, 40% socializer, 10% philanthropist, and 0% no-player. Additionally, Figure 1 shows the list of the considered Game Elements related to those Player Types. Following these changes in the player type classification and the addition of the non-player type, we adapted the Matching Matrix proposed by [PERSON] [15] in order to define the relationships between the new player types and the game elements most suitable for them (shown in Figure 1, where the higher the value of the cell, the more suitable the game element is). Note that empty cells contain zero values denoting the incompatibility of the game element with the player type. The combination of a Player Profile and the Matching Matrix produces one scored value per game element (Step 1 in Figure 1). Note that the selection of the top-scored game element slightly constrains the experience of the user, who may end up losing their gamification engagement. In the static adaptive gamification approach, since the Player Profile does not fluctuate during the experience, the top-scored game element will always be the same and the user will therefore only interact with a single game element, even if that game element varies in appearance or reward values.