# Engagement-Oriented Dynamic Difficulty Adjustment
## 5 Conclusions  
In this paper, we propose EDDA, a highly general, usable, scalable, and available Engagement-oriented Dynamic Difficulty Adjustment method. As a novel DDA solution, it aims to improve player experience by adjusting the corresponding stage's difficulty within the common parameter set based on observing players' gameplay per unit time in real time. EDDA is designed to center on player churn to adjust the game difficulty according to players' gameplay time. It introduces challenge, phase, and time in sleep, active, unit, and threshold dimensions. Furthermore, a common adjustable parameter set is constructed at the player, partner, opponent, and system levels based on the positive and negative correlations of game parameters and difficulty. The experiment with the prototype system demonstrates that EDDA has significantly improved the metrics of fitness, gameplay time, and GEQ scores compared with those without DDA. The results further prove the effectiveness of our proposed method in improving player experience, player engagement, and game balance. Based on this, game designers can keep track of players' situations during gameplay at any time and implement targeted solutions to prevent player churn while ensuring the optimal experience for players. It should be noted that we only verified the capability among relatively young players aged between 19 and 29 due to the restrictions of the long testing time. The applicability of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Group** & \(\boldsymbol{M}\) & \(\boldsymbol{Me}\) & \(\boldsymbol{SD}\) & \(\boldsymbol{z\,^{1}}\) & \(\boldsymbol{p\,^{1}}\) & \(\boldsymbol{\delta\,^{2}}\) \\ \hline \multirow{3}{*}{Fitness} & Baseline & 2.08 & 1.99 & 0.38 & \(-1.2677\) & 0.2049 & \(-0.3061\,^{\dagger}\) \\  & EDDA-s & 2.36 & 2.40 & 0.57 & \(-2.1129\) & 0.0346 \({}^{\ast}\) & \(-0.2653\,^{\dagger}\) \\  & EDDA & 2.73 & 2.50 & 0.79 & \(-2.1129\) & 0.0346 \({}^{\ast}\) & 0.4286 \({}^{\dagger}\) \\ \hline \multirow{3}{*}{Gameplay time} & Baseline & 181.15 & 164.34 & 68.23 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & \(-0.2653\,^{\dagger}\) \\  & EDDA-s & 214.66 & 190.15 & 71.22 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & \(-0.2653\,^{\dagger}\) \\  & EDDA & 236.55 & 201.92 & 80.32 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & 0.3878 \({}^{\ddagger}\) \\ \hline \multirow{3}{*}{GEQ scores} & Baseline & 16.41 & 19.14 & 5.66 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & \(-0.2245\,^{\dagger}\) \\  & EDDA-s & 18.78 & 20.89 & 5.81 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & \(-0.2653\,^{\dagger}\) \\ \cline{1-1}  & EDDA & 21.20 & 23.47 & 5.89 & \(-2.2819\) & 0.0225 \({}^{\ast}\) & 0.5510 \({}^{\lx@sectionsign}\) \\ \hline \hline \end{tabular}  
* The \(z\) and \(p\) values of each group are those of the Wilcoxon signed-rank test between itself and its next group. The order of groups is baseline, EDDA-s, and EDDA (loop). \({}^{2}\) The \(\delta\) of each group is calculated with itself as the first group and its next group as the second one. The order of groups is baseline, EDDA-s, and EDDA (loop). \({}^{\ast}\) The \(p\) value of fitness is less than 0.05, and those of gameplay time and GEQ scores fit the correction via the Holm-Bonferroni method. \({}^{\ddagger}\) The absolute value of \(\delta\) is greater than or equal to 0.147 and less than 0.330, which indicates a small effect size. \({}^{\ddagger}\) The absolute value of \(\delta\) is greater than or equal to 0.330 and less than 0.474, which indicates a medium effect size. \({}^{\lx@sectionsign}\) The absolute value of \(\delta\) is greater than or equal to 0.474, which indicates a large effect size.  
\end{table} 
Table 3: The metrics of post hoc pairwise comparison for baseline, EDDA-s, and EDDA. EDDA to players of other ages should be proven in subsequent work.