The Research Of Experts Scheduling Strategies In Mobile Context Learning

With the progress and development of smart mobile phones, mobile broadband network communication technology and Internet bandwidth, people’s learning styles and study habits are changing. People not only through learning in classroom, but also through the network using computer learning, even learning to use the trend of smart devices via mobile networks has become increasingly evident. In order to better meet the needs of mobile learning scenarios expert scheduling needs to study how effective and timely manner is one of the mobile scene scheduling expert learning platform needed to solve the problem, the scheduling policy for this article applies different expert, an expert on quality of service and learner-centered preferences expert scheduling policy.In this paper, while reading the relevant literature, combined with existing mobile learning scenarios encountered related demand platform for mobile learning scenarios under expert scheduling issues were discussed. First, study abroad to learn the theory of moving scene, describing the background and significance of the paper, in a lot of literature to read, based on the scenario elaborated mobile learning environment expert scheduling current research strategy. Then, on learning theory and learning modes are summarizing, briefly discusses the mobile learning platform scenarios, describes the relevant learning theory, scheduling theory and basic knowledge of service quality evaluation.A wide variety of learning problems of learners in the use of mobile learning scenarios, often in the learning process will be encountered. At this time, learners often alone cannot solve the problem, to help those who need guidance. In this paper, the learning environment under the platform, analysts scheduling conditions in mobile scenarios, the establishment of mobile learning scenarios expert scheduling model. Existing resources scenarios based on mobile learning platform to study how learners scheduling more appropriate expert resources in order to meet the urgent need expert guidance learners solve problems faced by the demand. Because the traditional scheduling methods cannot effectively meet the needs of the learning platform, this paper presents different scheduling strategies to address the needs of learners. This paper mainly consider scheduling policies affect the results produced, and from service fees and service quality learner concern raised these two angles strategy. Firstly, according to the conditions of mobile learning platform scenarios, combined with expert and expert services priced time together determine the service fees of experts to the fact that the services of experts proposed priority scheduling policy, and further increase the service fee model expert prioritization indicators formula. Then, the paper focuses on the impact of expert QOS scheduling effect, using questionnaires and data analysis methods, a detailed analysis of the factors affecting the quality of service evaluation experts. Then, in the expert QOS priority scheduling on the basis of the model, adding learners’ historical evaluation preference weights, by calculating the weight of preference rights, obtained an improved calculation method, namely learner preferences QOS priority algorithms. Finally, the services of expert priority, priority and precedence expert QOS Quality of Service learner preferences and other aspects, this paper established to solve the needs of learners expert scheduling policy. Its purpose is to improve scheduling accuracy, improved quality of service proposed expert guidance method for scheduling expert effect.In mobile learning scenarios, learners tend to learn a variety of learning problems encountered in the process. At this time, learners often alone cannot solve the problem, to help those who need guidance. This paper studies the solution platform based mobile learning scenarios, expert scheduling this issue in-depth research, and proposed priority services of experts, specialists QOS priority and quality of service priority learner preferences, these three scheduling strategies, and empirical experiment simulated the effect of their scheduling. Scenario-based mobile learning platform, using the original expert database data, process scheduling expert learner questions were simulated. Simulation scenarios for expert guidance on learner guide,which intended to enable learners to master the use of the computer’s motherboard troubleshooting card for fault detection. Through three strategies of expert-proposed scheduling and the quality of service priority strategy, service costs and quality of service priority strategy learner preferences priority scheduling strategy simulation experiments to study these three strategies of expert scheduling service impact on learner satisfaction, the comparative analysis of different strategies for scheduling expert evaluation of the pros and cons of the service. QOS learner preferences major priority scheduling policy priority to improve the quality of service policy expert, learner preferences by increasing the weight calculations made QOS priority strategy learner preferences, and in contrast to the experiments, to join the learner QOS priority policy preferences than experts QOS priority strategy better learner satisfaction, played a positive role.The paper also did a study on the future outlook, will revise and improve on the basis of expert evaluation factors on the quality of service, and data classification experts and learners request queue optimization, further refinement study. Meanwhile, after dispatching the appropriate expert learner, the learner will be how to ensure network quality and experts, reducing the amount of data transmission problems were studied.