# Evaluating Learner Engagement with Gamification in Online Courses
## 6 Results
That is, the students did not notice that some elements were interactive. Another measure of students' engagement with gamification is the average time that they spent interacting with game elements (\(Interaction\)_Time_). Figure 9 indicates a certain parallels with the previous figure (\(GE\)_Clicks_), especially in the low values for minutes. In fact, both measures are closely related (Mann-Whitney U, \(p\)-value = 0.27 \(>\) 0.05) because the more interactions students had with the game elements, the more time they spent on them. Nevertheless, measuring time is a non-trivial task as students may leave, for instance, the dashboard open while they are doing other things (such as getting up from the chair or chatting with other classmates). Note that in this case, we obtained some outliers (i.e., values higher than 70 min) as the right part of the figure reveals. We also gathered the score that students gave to the game elements (see \(Evaluations\) in the middle of Figure 5) that appeared throughout the course. To do so, we computed the mean score of each mechanic (normalized between 0 and 1) as the quotient between the sum of each score, \(s_{i}\), and the number of times, \(n\), the game element was shown. If a game element was shown more than one time to a particular user, we added the score for each game element occurrence. Figure 8: Comparison of \(GE\)_Clicks_. Left side: histogram. Right side: the mean of the number of interactions with elements and the variance of all students in each group. \"# users” means number of users. Figure 9: Comparison of Interaction Time. Left side: histogram. Right side: the average time students interacted with gamification elements and the variance of all students in each group. \"# users” means number of users. In general, DynamicAG scores equalled or slightly exceeded those obtained in StaticAG. Participants in both groups scored the mechanics almost equally (DynamicAG = 0.456, StaticAG = 0.483); indeed, this difference is non significant (Mann-Whitney U, \(p\)-value = 0.699). To further analyse the \(Evaluations\) of Game Elements, Figure 10 shows a histogram of scores detailed by game elements. The majority of students in both conditions gave a medium score to GEs, but scores higher than 0.65 only occurred in the dynamic adaptive gamification. As can be seen in the figure, several game elements have no score in StaticAG (badge, gift, lottery, development tool, knowledge share, lottery, and unlockable). The reason for this is that the fixed game elements corresponding to the initial player profile limited the number of mechanics shown to the students and, as a consequence, they were not able to score them. This was not the case for the DynamicAG group, where all the 14 game elements were shown and scored by participants. Specifically, game elements as shown in Table 1 with both changeable properties and appearances, such as Easter eggs and challenges, scored well. The low score of the badge element can be due to its visual design, which was similar, and probably the students were not able to perceive the differences. Thus, they believed that they were receiving the same badge all the time. Nevertheless, this is a supposition that should be proved. Moreover, there are game elements with changeable properties but fixed visual appearance (see Table 2), such as unlockable, lottery, development tools, and gifts, that obtained low scores. We think that, again, the discoverability of the elements may be the reason behind these results. If the users had been aware of how they functioned, their opinion could have been more positive. Finally, game elements targeting socializers (social status, social network) and the leaderboard were rated positively by participants.