-Administered at local centers in 120 countries throughout the world, IELTS (International English Language Testing System) is one of the most widely used large-scale ESL tests that offer listening, writing, reading and speaking modules. In mainland China, students join in various international joint-education program, in which IELTS preparation course generally lasts more than 2 or 3 semesters, to get easier access to better universities abroad. Because of its popularity and its use for making critical decisions about test takers when they apply for a prestigious foreign university, it is crucial to draw attention to their high frequency of retaking IELTS test and their comparatively low performance and low learning efficiency. Therefore, the present paper aims to provide a descriptive and critical review of various reasons underlying Chinese IELTS candidate's low performance, including the historical and cultural difference between Chinese and English, the immaturity of current IELTS preparation course, the inadequacy of academic research about IELTS. Consequential course-design issues will also be discussed and suggestions will be given for building high-efficient IELTS preparation course.

In order to further solve the problems in promoting the classification of media content in colleges and universities, the effective analysis and understanding of multimedia data content can be better realized based on the characteristics of multimedia data in colleges and universities, combining with the characteristics of rich information, large differences in performance, and large amount of large-scale data. This essay mainly introduces the technology of university media content detection and classification based on information fusion algorithm and focuses on the application of university multimedia content detection, analysis, and understanding, to explore the image discrimination auxiliary attribute feature learning and content association prediction and classification. A benchmark model for media content detection and classification is constructed. Through the model test, it is found that the F 1 value of the model is more than 70%, the check rate is more than 80%, and the recall rate is more than 50%. On this basis, a content detection system based on campus network is constructed.

Abstract. Learning analytics (LA) has been applied to various learning environments, though it is quite new in the field of computer assisted language learning (CALL). This article attempts to examine the application of learning analytics in the upcoming big data age. It starts with an introduction and application of learning analytics in other fields, followed by a retrospective review of historical interaction between learning and media in CALL, and a penetrating analysis on why people would go to learning analytics to increase the efficiency of foreign language education. As approved in previous research, new technology, including big data mining and analysis, would inevitably enhance the learning of foreign languages. Potential changes that learning analytics would bring to Chinese foreign language education and researches are also presented in the article.Keywords. learning analytics; big data; CALL; media The defining of learning analytics and how does it workIn higher education, businesses and governments, the data focus is increasingly expressed in the term learning analytics. Though still a young concept in education, learning analytics (LA) is frequently used in relation to data clusters. The ubiquity of the term analytics partly contributes to the breadth of meanings attached to it. A reasonable definition of learning analytics comes from the 1 st International Conference on Learning Analytics and Knowledge. It says, "Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purpose of understanding and optimizing learning and the environments in which it occurs." ( Banff and Alberta, 2011) Learning analytics, since then, has always been shaping the scope and range of activities in higher education, affecting administration, research, teaching and learning, and support resources. (Siemens and Long, 2011) Broadly speaking, a learning analytics program evaluate large data sets to provide decision makers or teachers with information that can help to improve learning outcomes, such as, grades, retention, or completion. LA collects and analyzes the "digital breadcrumbs" that students leave as they interact with various computer systems to look for correlations between those activities and learning outcomes. A professional LA software could do what the conventional data analysis software could not: it compares a students' activity with others in the class, with students who previously took the course, even with students who are in other countries but have this class online, to create a model for how each students is likely to fare. In this way, it could capitalize on the vast quantities of data to improve the efficiency of learning.

The current research attempts to compare the specific degrees of communicative properties of two types of College English Test Band 4 (CET-4) listening comprehension (before and after 2006 reform) in the light of IELTS (International English Language Test System) listening subtest, to know how well each of these three tests matches student's learning expectation and requirements, and to investigate student's psychological perception on them. This paper also tries to explore to what extent that 2006 reform has improved the communicative proficiency of CET-4, in terms of listening test. Based on quantitative and qualitative methods, it makes a comparative analysis between three listening subtests: old CET-4, new CET-4 and IELTS. Research subjects are 121 students from an international joint education program in a university in Beijing. Investigations into their performances are recorded and analyzed, together with a questionnaire survey. The present research finds that listening comprehension in new CET-4 has high degree of similarity with that in IELTS, particularly in testing of English communicative competence. With substantial amount of communicative properties, new CET-4 is a valid tool to measure student's listening proficiency.

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