# Engagement-Oriented Dynamic Difficulty Adjustment
## 4 Results and Discussion  
In the game industry, the primary game classification method is to classify according to its mode. As a result, the RPG, RAC, Fighting Game (FTG), Shooting Game (STG), Adventure Game (AVG), Strategy Game (SG), and Casual Game (CG) are the seven major genres widely researched and recognized by the whole industry. Based on this, a prototype system containing the above seven genres is implemented for the gameplay testing of players. Meanwhile, we introduce fitness, gameplay time, and GEQ scores as core metrics for evaluating game balance, player engagement, and player experience to verify the application effect of EDDA. Each item in the GEQ has \"No\", \"Maybe\", and \"Yes\", three options that correspond to 0, 1, and 2 scores, respectively. In the design of the ablation experiment (baseline (without DDA), EDDA-s (with EDDA's monitoring module and without EDDA's intervention module), and EDDA), three groups are selected for comparison since the intervention module cannot exist independently from the monitoring one. For EDDA-s, it adopts the same real-time monitoring algorithm as EDDA. However, it achieves DDA by directly adjusting the AI's overall skill level instead of specific parameters in the common parameter set as EDDA. We invited 100 participants (50 males and 50 females) aged between 19 and 29 (\(M\) = 22.96 and \(SD\) = 2.72) to test the prototype system for this study. Anonymity is assured in the study. We declare that the experiment was conducted with voluntary participation and fully informed participants. All participants should experience the seven games with the baseline, EDDA-s, and EDDA. Each genre's gameplay for each group lasted for two days, for a total of six weeks. Only after the experience of one group was finished would the next group continue. A demographic questionnaire was distributed to each participant before gameplay, while participants were to finish a GEQ corresponding to the group after two weeks of gameplay. Both games and groups were ordered by counter-balancing using random Latin squares, respectively. Six weeks later, we collected 100 log data and GEQ samples of the 100 players. [PERSON]'s alpha [31] shows strong reliability for the GEQ (\(\alpha\) = 0.81). The players' gameplay time in each genre was extracted from their log data to analyze and calculate fitness. The mean core metrics for the seven major genres of all players for each group are shown in Figure 2.  
By validating the methods through Friedman tests [32], significant differences are found for the gameplay time (\(\chi^{2}(2)\) = 14, \(p\) < 0.001) and GEQ scores (\(\chi^{2}(2)\) = 14, \(p\) < 0.001), while for the fitness they are not (\(\chi^{2}(2)\) = 5.43, \(p\) > 0.05). As shown in Table 3, Wilcoxon signed-rank tests [33] based on post hoc pairwise comparison with the Holm-Bonferroni method [34] show that each paired group exhibited significant differences in gameplay time and GEQ scores, including EDDA versus EDDA-s, EDDA versus baseline, and EDDA-s versus baseline. The comparison of medians as well as means demonstrates the effectiveness of EDDA's monitoring and intervention modules in enhancing gameplay time and GEQ scores. By contrast, fitness only performs the Wilcoxon signed-rank test. The results prove that the intervention module can improve difficulty fitness. Moreover, we utilized [PERSON]'s delta (\(\delta\)) [35] to further measure the differences in the paired groups. The monitoring and intervention modules, respectively, show a small effect size from the results, while the two modules combined have larger ones. Thus, the significant differences in all the metrics between EDDA versus baseline are verified on all sides. Via the systematic analysis of the experimental results, we can conclude that EDDA's real-time monitoring algorithm and common parameter set improve player experience and engagement significantly, and that the parameter set can further ensure game balance to a certain extent.