# Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices
## 5 Results and Discussion  
### Results  
We obtained the task time, number of scrolling actions, and number of overshoots from the user study. We calculated the average for each condition at the end of all test sessions and compared the results of AKS with those of standard scrolling to answer the research questions. **RQ. 1 How effective is AKS in reducing task time compared to standard kinetic scrolling? ** First, we verified that using AKS aids users in accessing required information more rapidly (see Figure 6). When using AKS, the average task time was reduced by approximately 14-25%. We observed the effect of AKS to be significant, particularly when the target distance is long. In the case of a task with a target distance of 800 items, the average task time of AKS was reduced by approximately 25% compared to the average task time of standard scrolling. It was also verified that the task time was reduced by approximately 23% when the target distance was 600 items. As a result, AKS appears to be useful for reducing the time required for repetitive scrolling actions to access specific data. The results in Figure 6 show that AKS can help users to access target information faster. However, given that the scale of our experiments is limited, additional statistical analysis is needed. To verify these results, we conducted a \(t\)-test to confirm that there is a significant difference between the task time of the AKS and the standard scroll. First, we conducted the Shapiro-Wilk test (significance level = 0.05) to check the normality of the collected data group for each target distance. Through this test, we have confirmed that each dataset is normally distributed. Subsequently, we performed a \(t\)-test (significance level = 0.05) using the same datasets. Table 2 shows the standard deviation and \(p\)-value for each target distance. Based on the calculated \(p\)-value, the difference in task time between the two scrolling types is statistically significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Target Distance**} & \multicolumn{3}{c}{**Standard Deviation**} \\ \cline{2-4}  & **Standard Scroll** & **AKS** & \\ \hline
200 & 1.324 & 0.836 & \textless{}0.001 \\
400 & 1.184 & 1.074 & \textless{}0.001 \\
600 & 1.896 & 1.472 & \textless{}0.001 \\
800 & 1.571 & 2.119 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Standard deviation (\(SD\)) and \(p\)-value of task time. Figure 6: Time required for scrolling tasks. **RQ. 2 How effective is AKS in reducing the number of scrolling actions compared to standard kinetic scrolling? ** In addition to the task time, we expected that the scrolling action performed by the user would be reduced when AKS was used. This is because scrolling becomes more rapid through AKS; therefore, the number of actions required to reach the target data can be reduced. The number of actions were reduced in general. When using AKS, the user performed approximately 2-10% fewer actions (see Figure 7). Similar to the task time, the larger the target distance, the more effective AKS appeared to be. When the data to be accessed is not far away, significant results could not be observed when applying AKS. For example, if the target distance is 200 or 400, only minor improvements are exhibited. However, when the target distance was 600 or 800, the effect was significantly higher. This tendency was also verified in **RQ. 1**. Moreover, compared to the task time, the improvement in the number of actions appears relatively marginal. Like in **RQ. 1**, a \(t\)-test was performed using the collected data to verify the results. We used the Shapiro-Wilk test to check the normality of the collected datasets (significance level = 0.05). Using this test, we confirmed that all datasets were normally distributed. Furthermore, a \(t\)-test (significance level = 0.05) was performed to verify the differences in number of actions for two scrolling types. Table 3 shows the standard deviation and \(p\)-value for each target distance.