multi-media technology plays a critical role in modern remote education development, which can make abstract and complicated teaching content more concrete and fully mobilize students’ activity, enrich the teaching resources of remote education, break through the space-time limitation of traditional education, and meanwhile strengthen the interaction of remote education. Currently ordinary remote teaching system based on Dekeos and Bigbluebutton without self-detection function requires for professional personnel’s regular examination and maintenance. It takes long time and large workload, brings inconvenience for schools and teachers in remote education. Meanwhile, it also has the problem of disjoint between teaching and practice. This paper has designed a multimedia teaching system based on Dokeos and BigBlueButton to apply to“Fundamentals of Financial Accounting” course including abundant remote education functions such as recording and replaying, desk sharing, video session, PPT presentation. Student condition online inspection device of remote education based on decision-making tree has solved the problem existing in current remote teaching system that it cannot automatically inspect device, thus to further optimize the process of remote education.
The paper presents an evolution of personalized courses based on genetic algorithms (PCEGA). The genetic algorithm are successfully applied in the dynamic update process of the course during the whole learning process. Under this framework of this algorithm, the target user model updates dynamically, and the courses evolve during the process. It provides a good general purpose and scalable framework that addresses the personalized course generation in an online learning environment.
Network congestion control problem is one of the most key problems in network study. This paper studies different versions of TCP source port algorithm such as Tahoe, Reno, NewReno, SACK and Vegas, and makes simulation research on linear network and dumbbell network environment algorithm by using NS2 network simulator, and finally it concludes that: when there is no competition in the network, it is better to choose Vegas algorithm; when there is competition, choose Reno, NewReno, or Sack algorithm is more advisable.
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