Peer grading, allowing students/peers to evaluate others’ assignments, offers a promising solution for scaling evaluation and learning to massive open online courses (MOOCs) and small private online courses (SPOCs). In the environment of MOOCs, due to the varied skill levels and attitudes of online students, it is not easy for the students to present fair and accurate scores for their peers’ assignments. Recently, statistical models have been proposed to improve the fairness and accuracy of peer grading, and these models have achieved good performance in MOOCs. However, our experiments demonstrate that these models fail to deliver accurate inferences in the SPOC scenario because affinity among students may seriously affect the objectivity and reliability of students in the peer-assessment process. To address this problem in SPOCs, this paper proposes a human-machine hybrid peer-grading framework, in which an CNN(Convolutional Neural Networks)-based automated grader works as a filter to ensure reasonable peer scores before Bayesian models are utilized to infer the true scores. This framework can significantly eliminate the severely biased scores of undutiful students and, thus, improve the accuracy of the true-score estimation of the Bayesian peer-grading models. Both the simulated and actual peer-grading datasets in our experiments demonstrate the effectiveness of this new framework for SPOCs.

Peer grading has diverse applications in many fields, including the peer grading of open assignments in online courses. The major challenge in peer grading is improving the seriousness (review carefully) of reviewers. The previous studies have proposed a number of incentive reward mechanisms intended to reward or punish reviewers. Although these mechanisms are effective in massive open online courses environments with a large number of students, they are not suitable for small private online courses environments with a small number of students. This study analysis the characteristics of reviewers in a small private online course environment and designs an incentive and reward mechanism based on a hybrid human-machine peer grading framework, including an auto-grader (reviewed by the machine). The main component of the proposed mechanism is the auto-grader whose function is to evaluate the reviewers' reports (reviewers refer to students) and provide feedback information on reports to reviewers. The framework can incentivize reviewers to review carefully and give their reports truthfully when the reports between reviewers' and the auto-grader' are consistent. The proposed framework is verified experimentally. The experimental results demonstrate that the hybrid human-machine peer grading framework can effectively incentivize reviewers to review submissions carefully and improve the overall effect of peer grading.

In this work, we studied the online blended learning model of computer network experimentation, focusing mainly on the problem of traditional network experiments being limited by location and time, and explore the applicability of the small private online course (SPOC) advanced teaching concepts to computer network online experiment teaching. Based on the structure of a combination of virtual and real, real and not virtual, an online network experiment platform and management system has been designed and constructed, enabling students to carry out remote online computer network hardware experiments anytime and anywhere, without being restricted by time, space, or content. Using the online network experiment platform, we can organize the experimental modules and knowledge points via the SPOC course concept, by developing online network experimental content, modularizing and fragmenting of the experiments, creating the pre-experimental explanation and experiment preview videos, and evaluating the assignments via peer grading to analyze students’ learning behavior. By exploring online network experimental teaching methods and management models, offering experimental guidance in an interactive manner, and highlighting the openness and sharing characteristics of online experimental teaching platforms, we can improve the utilization rate for teaching resources, and provide ideas for applied scientific research methods.