# Online Blended Learning in Small Private Online Course
## 5 Online Blended Learning Based on Network Experiment Courses
### Assignment of Tasks in Peer Grading
### The Promotion of Peer Grading to the Benefit of Network Experimental Teaching

By solving the four problems in peer grading: the evaluation of students' cognitive ability, task assignment in peer grading, removing the noise data in the peer grades, and improving students' conscientiousness, the online SPOC course helps students to gain a deep understanding of network experimentation, and while strengthening their hands-on ability, it also helps students to understand the inner principles of the experiment. #### 6.2.1 Experimental Results of the Grade Aggregation Model Based on Ability Assessment

In this paper, we compare the results of our PG6-PG8 model with those of a simple median model based on the baseline, as well as with the PG1-PG3 models proposed in [7] and the PG4-PG5 models defined in [8]. The evaluation index is the root mean square error (RMSE), which is calculated via the deviation between the ground truth grade given by the TAs and the peer grade given by the student grader. In the review of students' abilities, since the first and last problems in peer grading are related to visualization, in order to make the experiment simple, we only assessed the second, third, and fourth questions. The experimental results shown in Tables 2 and 3 are the average RMSE values of these models over 10 experiments. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Problem 2**} & \multicolumn{3}{c}{**Problem 3**} & \multicolumn{3}{c}{**Problem 4**} \\ \cline{3-8}  & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} \\  & & & **Deviation** & & **Deviation** & & **Deviation** \\ \hline Baseline & Median & 17.65 & — & 13.75 & — & 13.23 & \\ [PERSON] et al. 2013 [7] & PG1 & 14.43 & 0.02 & 12.76 & 0.04 & 11.69 & 0.02 \\  & PG3 & 14.25 & 0.03 & 12.43 & 0.03 & 11.54 & 0.04 \\ [PERSON] et al. 2014 [8] & PG4 & 14.28 & 0.02 & 12.46 & 0.06 & 11.32 & 0.06 \\  & PG5 & 14.35 & 0.03 & 12.42 & 0.04 & 11.30 & 0.07 \\  & PG6 & 14.33 & 0.09 & 12.45 & 0.04 & 11.35 & 0.05 \\ Models based on ability & PG7 & 14.36 & 0.08 & 12.43 & 0.05 & 11.31 & 0.06 \\  & PG8 & 14.33 & 0.09 & 12.39 & 0.06 & 11.27 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results for the peer grades given by the graders who did not make submissions, without filtering. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Problem 2**} & \multicolumn{3}{c}{**Problem 3**} & \multicolumn{3}{c}{**Problem 4**} \\ \cline{2-9}  & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} \\  & & & **Deviation** & & **Deviation** & & **Deviation** \\ \hline Baseline & Median & 1822 & — & 14.41 & — & 14.21 & \\ [PERSON] et al. 2013 [7] & PG1 & 15.63 & 0.02 & 12.56 & 0.04 & 12.40 & 0.03 \\  & PG3 & 15.58 & 0.01 & 12.37 & 0.05 & 12.33 & 0.01 \\ [PERSON] et al. 2014 [8] & PG4 & 15.67 & 0.08 & 12.40 & 0.07 & 12.35 & 0.08 \\  & PG5 & 15.58 & 0.07 & 12.33 & 0.09 & 12.32 & 0.05 \\  & PG6 & 15.58 & 0.01 & 12.35 & 0.06 & 12.36 & 0.04 \\ Models based on ability & PG7 & 15.60 & 0.04 & 12.37 & 0.07 & 12.38 & 0.05 \\  & PG8 & 15.50 & 0.08 & 12.29 & 0.09 & 12.35 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the experiment assessing the initial ability of a grader who did not offer a submission. In our experiments, we found that if the student graders who did not submit submissions were given random grades depending on their previously assessed ability, the fluctuations in predicted grades would increase, because if there is no accurate previous estimate of a students' ability, it is difficult to give a reasonable estimated grade for the submission. It can be seen from Tables 2 and 3 that the average performance of PG8 is the best among all the models.