# Online Blended Learning in Small Private Online Course
## 5 Online Blended Learning Based on Network Experiment Courses
### Assignment of Tasks in Peer Grading
The best results can be obtained by combining the grader's grading ability and the real grade with appropriate weights. Therefore, the PG8 model is the best approach to aggregating the peer grades in SPOCs. This section aggregates the peer grades based on this ability assessment method, then evaluates the effect of students participating in peer grading, provides reasonable grades for the experimental online submissions of students, and in so doing reduces the teachers' burden. #### 6.2.2 The Results of the Task Assignment Experiment for Peer Grading

Figure 9 shows the experimental results of using the MLPT algorithm for peer grading. The ordinate represents the reduction in the variance of the grading group after replacing the random algorithm with the MLPT algorithm. It can be seen that the MLPT algorithm is effective in reducing the variance in the grading group level, especially when the number of participating students is small and the redundancy is small, under which conditions the advantages of the MLPT algorithm are more prominent. The MLPT algorithm solves the problem of unreasonable task allocation in peer grading. #### 6.2.4 The Results of Eliciting Truthfulness with Machine Learning

(1) Comparison of error statistics experiment results with and without autograder. We compared the results of a peer grading process using an autograder and the same with no autograder in the BGP experiment of the network experiment class, and we found that the autograder can motivate the graders to review more carefully. In Figure 10, we set the number of students participating in the experiment, the number of peer grades, and the interval between the horizontal and vertical axes in the histogram in the two graphs to be equal. It can be seen from the figure that after adding an autograder as the object of the peer prediction, the number of graders with larger error values is significantly reduced, and especially those with an error value of 35-50. The resulting peer grades contain very little noise, so they can be effectively applied to the PG model. (2) Comparison of RMSE when autograder is added and when it is not added

In Table 5, the error grade is taken as the absolute value of the difference between the grader's peer grade and the final real grade for the same submission. The purpose of the RMSE in the table is to measure the degree of dispersion of the data. It can be seen from the table that an autograder was not added to experiment 4 in the fall of 2020, while it was added in the autumn of 2019. The error mean and standard deviation of the latter both decreased, and the RMSE value decreased by 6.39 points, or 82.77%. The data show that after adding an autograder, the gap between the peer grade and the actual grade is significantly reduced, which means that the quality of student peer grading is significantly improved. By eliciting truthfulness via machine-learning in the peer grading process, the seriousness with which students take peer grade is effectively improved. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{2-6}  & \begin{tabular}{c} **Initial Data Set** \\ **without Filtering** \\ \end{tabular} & \begin{tabular}{c} **Original Data Set** \\ **Based on Naive** \\ **Bayes Autograder** \\ \end{tabular} & \begin{tabular}{c} **Data Set with** \\ **Random Filtering** \\ \end{tabular} & \
\begin{tabular}{c} **Data Set with** \\ **Anomaly** \\ **Detection** \\ \end{tabular} &
\begin{tabular}{c} **CNN-Based** \\ **Autograder** \\ **Dataset** \\ \end{tabular} \\ \hline PG1 & 21.34 & 21.90 & 21.05 & 16.89 & 12.54 \\ PG3 & 20.12 & 22.02 & 21.10 & 16.31 & 11.43 \\ PG4 & 21.90 & 22.43 & 22.30 & 17.20 & 11.32 \\ PG5 & 21.65 & 21.67 & 20.50 & 17.30 & 12.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the human-machine framework. Figure 10: Comparison experiment with and without autograder. (**a**) We added autograder to the peer grading in the autumn of 2020.