# Online Blended Learning in Small Private Online Course
## 5 Online Blended Learning Based on Network Experiment Courses
### Assignment of Tasks in Peer Grading
Here we define \(\mathrm{p}(s^{r}_{auto})\) as the accuracy of autograder, and the probability that autograder chooses A, B, C, and D is equal, that is,

\[\mathrm{p}(\mathrm{A})=\mathrm{p}(\mathrm{B})=\mathrm{p}(\mathrm{C})=\mathrm{p }(\mathrm{D}) \tag{9}\]

Equation (9) is the previously assessed probability of a topic being evaluated correctly, and \(\mathrm{p}(\mathrm{a}|\mathrm{A})\) is the probability of the grader \(i\) reporting \(a\) when the prior value is A, assuming that \(f(a|A)\), \(f(a|B)\), \(f(a|C)\), and \(f(a|D)\), the probability of the grader \(i\) choosing \(a\) is:

\[\mathrm{Pr}(s^{a}_{i})=f(a|A)p(A)+f(a|B)p(B)+f(a|C)p(C)+f(a|D)p(D) \tag{10}\]

Thus the probability of the autograder's review result being \(a\) is:

\[\mathrm{g}(s^{a}_{auto}|s^{a}_{i})=\mathrm{f}(a|A)\frac{f(a|A)p(A)}{\mathrm{ Pr}\big{(}s^{a}_{i}\big{)}}+f(a|B)\frac{f(a|B)p(B)}{\mathrm{Pr}\big{(}s^{a}_{i} \big{)}}+f(a|C)\frac{f(a|C)p(C)}{\mathrm{Pr}\big{(}s^{a}_{i}\big{)}}+f(a|D) \frac{f(a|D)p(D)}{\mathrm{Pr}\big{(}s^{a}_{i}\big{)}} \tag{11}\]

The result calculated from Equation (11) is the probability of the autograder obtaining \(a\), which is converted from a probability to a grade. We use the logarithmic grading rule, as shown in Equation (12):

\[\mathrm{score}(s^{n}_{auto}|re_{i})=\ln(\mathrm{g}(s^{n}_{auto}|re_{i})) \tag{12}\]

Here, \(n\) represents the number of categories of answers in a question. We use the report given by grader \(i\) to predict the grade value of the autograder. Following the above calculation of the autograder's grade value, Equation (11) is used to predict the reward for grader \(i\):

\[\sum_{n=1}^{4}\mathrm{score}(s^{n}_{auto}|re_{i})\mathrm{g}(s^{n}_{auto}|s_{i}) \tag{13}\]

The only way to maximize Equation (13) is to make \(re_{i}=s_{i}\). Because the autograder only gives its own review results, if the grader \(i\) wants to get the maximum reward, the report they give must be consistent with what they really think. ## 6 The Learning Effect of Online Blended Learning

Our online blended learning method of computer network experimentation improves the learning effect, optimizing the teaching mode of the network experiment, improving experimental knowledge through peer grading, and clearly improving exam grades. ### Optimize the Teaching Mode of Network Experiment

In the traditional teaching mode, students can only go to the network laboratory to undertake experimental learning, and their learning is inevitably restricted by time and space. A considerable part of the experimental time is occupied by traditional verification and analytical experiments, and time and conditions are insufficient to carry out more difficult research and innovation experiments. Via the online learning model, it is easy to carry out experiments of medium or lower difficulty as online experiments. As part of the preparation process, students are required to complete the experiment online before participating in the experiment in class. Teachers and TAs provide remote online experiment guidance. After students have completed the basic online experiments, they can conduct comprehensive analytical and design experiments in the laboratory, and can even design experimental content independently. The classroom is mainly for one-to-one guidance and small-scale discussions. By offering pre-experiment explanations and experimental previews in a video format, the online blended learning method combining online experiments, offline experiments, and peer grading greatly improves the teaching effects, with students able to complete basic online experiments later. Comprehensive design experiments with a certain degree of difficulty are directly carried out in the laboratory. This not only broadens the content of experimental teaching, but also increases its depth and difficulty.