A New Intelligent Optimization Network Online Learning Behavior in Multimedia Big Data Environment

Introduction

In the era of multimedia big data, the online learning behavior of users becomes rich and colorful. Rich audio, video, text, images and other big data, allowing users to acquire knowledge more convenient way (Barnich & Droogenbroeck, 2011). Due to the exponential growth of the information technologies and the quantity of data proliferates rapidly, we are quite difficult to analysis the huge amount of data. In multimedia big data, the transmission, storage, processing and application of image and video are four key technical problems (Wang, 2015). The rapid development of information technology represented by the Internet has changed the way people get information and disseminate knowledge. The traditional classroom teaching mode has been unable to meet the needs of modern lifelong learning due to the lack of interaction and personalized learning. And with Web2.0 as the core of online learning to provide inter temporal learning environment and rich educational resources for learners, learners between collaborative learning and resource sharing, to guide the future development direction of education.

With the rapid growth of databases, data mining has become an increasingly important approach for data analysis (Achanta et al., 2012). With the popularization and application of Internet and network technology, human learning activities become more and more personalized, virtual and collaborative, showing different characteristics from traditional learning (Droogenbroeck & Paquot, 2012; Varadarajan & Zhou, 2013). Especially in the network environment, learners' learning behavior reflects the multi structure and multi-level characteristics (Amir Hosein et al., 2009). It is of great significance for the construction of personalized and intelligent learning system to conduct a thorough and comprehensive study on network learning behavior. If we can solve the problem of tracking, collecting, analyzing and evaluating the learner's behavior, it will help us to develop a personalized and intelligent adaptive network learning system.

With the development of the computer network technology, the distance education based on Internet has become a new developing application. Research on the instruction and learning based on Internet has become a hot focus, too. Research on the E-learning behavior, will be benefit to the organization, the management and the guidance of the course under the terms to network for teachers, will be benefit to the development of educational resources and platform too (Gandal, 2001). Data today comes in all types of formats. Structured, numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data are something many organizations still grapple with. Data mining is one of the algorithm commonly used to identify, validate and prediction of data. The risk of data classification using knowledge obtained from known historical data has been one of the most intensively studied subjects in statistics, decision science, operation research, and computer science.

With the online learning environment, more and more multimedia and online learning researchers and practitioners are increasingly concerned about the positive effects of multimedia tools, teaching methods of online learning media. E-learning behavior is carried out on the Internet for a multi-level study by means of a student; learning behavior is self-discipline, self-control, behavior subject has the autonomy to decide the learning goals, learning progress, learning strategies, and the study for the occurrence and development and change of learners; can make use of various network provides convenient communication tools, the theme of discussion and exchange, the realization of online collaborative learning; learners in the learning process, can be obtained from teachers and partners, subject experts, learning support system support and help in a timely manner(Jiang et al., 2005).