# Evaluating Learner Engagement with Gamification in Online Courses
## 7 Discussion  
Overall, our results show that approaches to gamification that take into account the player profiles have a positive impact on the user experience. Indeed, these results are in line with previous research [15]. However, we went further, updating the player profile during the experience. Our findings demonstrate that the dynamic approach, compared to a static player profile, fits students' preferences better, as proved by the differences found in the metrics of engagement with gamification. Moreover, we used weighted random probabilities to select, from the entire set of GEs, the one to show to the student in both the static and dynamic approaches (see Selection 1 in Figure 1), making the static approach more \"dynamic\" than a purely static approach that would have selected the game element with the highest score (see Selection 1 in Figure 1). Figure 10: \(Evaluations\), i.e., mean scoring of GEs grouped by GE type. The left side includes Changeable GEs (in both properties and appearances). In the middle, GEs that only change their properties according to the learning progress. On the right side, GEs with fixed appearances and properties. Notice that our study could not evaluate the behaviour of non-player profiles because there were no participants who chose not to \"play\", as expected with 15-16-year-old participants. Even so, the results of the DynamicAG group surpassed those in StaticAG. Throughout the development of this project we learned some lessons to share regarding different aspects: (i) the setup of the DynamicAG approach, (ii) the instructional design of the course, and (iii) the design of gamification elements. First, in relation to the DynamicAG approach, there are certain parameters used to change the Player Profile depending on the interactions and opinions of the students regarding game elements. To avoid high fluctuations in the Player Profile values, our method manages small values of these two parameters. However, these small values provoked few variations in the player profile. Consequently, it is necessary to revisit the method for tuning parameter values. Second, another aspect to be considered is the influence of the instructional design. We proposed this experiment in a nanoMOOC, which is a short and very focused learning itinerary. Therefore, the adaptive gamification method had to tune the Player Profile with limited data in a limited time. A possible extension is to maintain the gamification across several courses and thereby collect new data to continually improve the Player Profile model. Third, an important factor is the design and distribution of the game elements across the span of the instructional activities. In the gamification design, we considered the incorporation of game elements after the most arduous activities. However, the gamification elements also require feedback regarding the student performance in those activities in order to adapt their visual aspect or to change their properties, such as rewards. This fact implies that activities that cannot be tracked by the learning platform, such as reading a pdf, cannot be rewarded with a game element. Moreover, both adaptive gamification approaches automatically select the game elements to display, but the DynamicAG performed better. First, it distributed a wider range of GEs than the StaticAG. Second, the GEs were generally scored higher by students. Future studies may analyse the impact that the changes in appearance and properties of GEs have on the whole experience. Additionally, the architecture of the OpenEdX platform has some limitations. For instance, when students finish an activity, they must select the _I want an award_ button to display the Game Element, causing users to probably overlook such game elements.