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Computer-supported collaborative learning (CSCL) is a learning strategy that gathers students together on campus through mobile application software on intelligent handheld devices to carry out creative exploration learning activities and social interaction learning activities. Learning resource diffusion is a very important constituent part of CSCL mobile software. However, learners will receive or forward a large number of learning resources such that short video, images, or short audio which will increase the energy consumption of forwarding nodes and reduce the message delivery success rate. How to improve the message delivery success rate is an urgent problem to be solved. To solve the aforementioned problem, this paper mainly studies the diffusion of learning resources in campus opportunistic networks based on credibility for CSCL. In campus opportunistic networks, learners who participate in collaborative learning can obtain the desired learning resources through the distribution and sharing of learning resources. Learning resource diffusion depends on the credibility of learners who participate in collaborative learning. However, the existing classical algorithms do not take into account the credibility between learners. Firstly, the concept of credibility in campus opportunistic networks is proposed, and the calculation method of credibility is also presented. Next, the problem of node initialization starvation is solved in this paper. The node initialization starvation phase of collaborative learning is defined and resolved in campus opportunistic networks. Based on the information of familiarity and activity between nodes formed in the process of continuous interaction, a learning resource diffusion mechanism based on node credibility is proposed. Finally, the paper proposes a complete learning resource diffusion algorithm based on credibility for computer-supported collaborative learning (LRDC for short) to improve the delivery success rate of learning resources on the campus. Extensive simulation results show that the average message diffusion success rate of LDRC is higher than that of classical algorithms such as DirectDeliver, Epidemic, FirstContact, and SprayAndWait under the different transmission speed, buffer size, and initial energy, which is averagely improved by 46.83%, 44.43%, and 45.6%, respectively. The scores of LRDC in other aspects are also significantly better than these classical algorithms.

LRDC: Learning Resource Diffusion Based on Credibility for Computer-Supported Collaborative Learning