# Online Blended Learning in Small Private Online Course
## 5 Online Blended Learning Based on Network Experiment Courses
### Assignment of Tasks in Peer Grading  
(1) Task assignment in peer grading  
In peer grading, unreasonable task assignment has a great influence on the aggregation of grades. In peer grading, each student plays two roles, as both a submission submitter and a submission reviewer. The essence of the grading task assignment problem is how to form a grading group. Many online experiment platforms use simple random strategies and do not consider differences between students. In fact, different students have different knowledge levels, so the reliability of grading also differs. Random assignment may result in some grading groups containing students with better knowledge, while other groups are composed of students with lower levels of knowledge, which thus affects the overall accuracy of homework grading. (2) MLPT-based task assignment model  
There are many similarities between the grading submission assignment problem and the task scheduling problem, so the LPT algorithm also provides a good solution for the former. The problem of assignment can be understood from another perspective. Assigning submissions to students for review is equivalent to assigning students to assignments for review, so the student set \(V=\{v_{1},v_{2},v_{3}\ldots v_{n}\}\) can be regarded as the set of tasks to be assigned in the scheduling problem \(J=\{j_{1},j_{2},j_{3}\ldots j_{n}\}\). The knowledge level \(w(v_{i})\) of each student is understood as the processing time \(t(j_{i})\) required by the task, and the submission set \(U=\{u_{1},u_{2},u_{3}\ldots u_{n}\}\) is understood as the set of processors \(M=\{m_{1},m_{2},m_{3}\ldots m_{p}\}\). Therefore, in the task scheduling problem, the tasks are assigned to the machines to ensure that the running times of each are as similar as possible, which is equivalent to assigning students to submissions in such a way as to ensure that the knowledge levels of the reviewing students are as similar as possible. By integrating the similarities and differences of submission assignment and task scheduling, we propose an MLPT (modified LPT) algorithm to solve the submission assignment problem in peer grading. Its pseudo code is shown in Algorithm 1. ```
1: Sort the graders according to their knowledge level so that \(w(v_{1})\geq w(v_{2})\geq\ldots\geq w(v_{n})\)
2: For \(i=1,2\ldots,n\): A collection \(U(i)\) of assignments reviewed by students \(v_{i}\)\(U(i)\) is empty, \(U(i)\leftarrow\varnothing\)
3: For \(j=1,2\ldots,n\): The comprehensive level \(L_{j}\) of the grading group corresponding to submission \(u_{j}\) is assigned 0, \(L_{j}\gets 0\)

Assignment \(u_{j}\) corresponds to the student set \(V(j)\) of the grading group to be empty, \(V(j)\leftarrow\varnothing\)

4: For \(i=1,2,\ldots,n\): For \(k=1,2,\ldots,m\): In the assignment set \(\left\{u_{j}\middle|j\
eq i\right\}\), select the assignment with the smallest comprehensive level of the corresponding grading group, and mark it as \(u_{j}\),

Add current student \(v_{i}\) to the grading group corresponding to homework \(u_{j}\), \(V(j)\gets V(j)\cup v_{i}\),

Update the comprehensive level \(L_{j}\) of the grading group corresponding to the assignment \(u_{j}\), \(L_{j}\gets L_{j}+w(v_{i})\),

Add submission \(u_{j}\) to the student set \(U(i)\) reviewed by student \(v_{j}\), \(U(i)\gets U(i)\cup u_{j}\). ### Peer Grading Model Based on Human-Machine Hybrid Framework

(1) The problem of a large number of random grades in peer grading

The probability graph model that is suitable for peer grading in MOOCs may not perform well in SPOCs. Because students know each other in an SPOC environment, they often assign a random grade to a given assignment, rather than reviewing it seriously. In fact, we found that some graders simply give the submission a full mark or a high grade in order to complete the task quickly. This kind of careless review behavior prevents the basic assumptions in the Bayesian statistical model from being met, and will inevitably produce data noise that reduces the performance of the model.