## 5 Conclusions  
The background of this research was the development of unmanned and autonomous ships. Due to the present trend of increasing the responsibility of navigators and decreasing the required number of human operators, there are questions about automated systems in terms of safety [24]. In addition, the responsibility in urgent situations stays on the human operator; it is necessary to develop a proficient evaluation model for future navigators. Hence, the authors focused on evaluation methods of simulated ship-handling training, especially on average steering angle in altering. Unlike other research using threshold-based criteria, the proposed model achieved comparable expert data as evaluation criteria. Probabilistic methods accurately estimated and populated non-covered expert data, and the trendlines of average steering angle recovered. Afterward, sample evaluation was conducted using the trainee's simulated ship-handling training result. The research used the hidden Markov model beside the neural networks in the estimation. As this research aims to populate the expert data for educational purposes using a simulated environment, the process of the proposed model has to be explainable and expendable. In the proposed methods, the data generated during the process stay. Hence, the additional evaluation criteria, such as the experts' steering sequences, can be used further. Moreover, the HMM model is based on the transition of states from actual navigators' steering and course alteration. Hence, the result of any course alteration would be in the range of actually conducted ones. Taking those as an advantage, even the neural network models could have better estimation performance; as mentioned earlier the research used the hidden Markov model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\begin{tabular}{c} **Altering** \\ **Angle (deg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elapsed Time** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Trainee’s** \\ **Average** \\ **Sleering** \\ **Angle (deg)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Range of** \\ **Experts’** \\ **Average** \\ **Sleering** \\ **Angle (deg)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Result** \\ **Sleering** \\ **Angle (deg)** \\ \end{tabular} } \\ \hline
1 & \(-2\) & 32 & 1.7 & \(-5.0\sim 0.3\) & Failed \\
2 & 7 & 38 & 0.2 & \(0.0\sim 4.6\) & Passed \\
3 & \(-15\) & 45 & \(-3.2\) & \(-7.9\sim-2.7\) & Passed \\
4 & \(-10\) & 77 & \(-0.6\) & \(-4.0\sim-1.1\) & Failed \\
5 & \(-12\) & 65 & 0.9 & \(-4.3\sim-1.4\) & Failed \\
6 & 13 & 73 & 1.4 & \(1.2\sim 5.6\) & Passed \\
7 & \(-30\) & 82 & \(-4.8\) & \(-9.0\sim-4.2\) & Passed \\
8 & 9 & 106 & 0.6 & \(0.0\sim 3.8\) & Passed \\
9 & 50 & 100 & 8 & \(7.4\sim 13.0\) & Passed \\
10 & \(-38\) & 112 & \(-2.5\) & \(-7.0\sim-3.7\) & Failed \\
11 & \(-19\) & 109 & \(-1.6\) & \(-3.9\sim-1.6\) & Passed \\
12 & 8 & 160 & 0 & \(0.0\sim 1.5\) & Passed \\
13 & 19 & 161 & 0.7 & \(0.4\sim 3.7\) & Passed \\
14 & 10 & 123 & 0.1 & \(0.0\sim 1.8\) & Passed \\
15 & \(-7\) & 125 & \(-0.3\) & \(-1.8\sim 0.0\) & Passed \\
16 & 34 & 145 & 2 & \(1.4\sim 7.2\) & Passed \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the “Average steering angle” between the trainee and experts. Limitations and future works remain for further consideration. As a limitation of the present research, even though the proposed model populates the experts' data, the data are still restricted to the original ship's movement tendency. The tendency is inevitably influenced by the environmental factors of the target ship, such as the water area and traffic route. In future work, the ship's speed has to be sophisticatedly measured in the probability calculation, because the course alteration differs along the speed. External forces such as wind and current should also be considered as well. Additionally, the model still requires a single target ship's original data for each type of ship.