Research On The Relationship Between Distance Learners’Learning Participation And Academic Performance

Since1999,China’s Ministry of Education has approved68colleges and universities to carry out modern distance education pilot. After more than ten years of development, China’s distance education scale expanded rapidly. With the continuous expansion of the scale, due to the relative spatial and temporal separation of teachers and students that essential attribute of spatial and temporal behavior of teaching and learning activities as well as the learner’s own contradictions between work and study allows remote difficult to guarantee the quality of education. For this reason, more and more distance education institutions and practitioners began to focus on a specific distance learners’learning process and academic performance.In modern distance education, learners’ learning process is composed of learners’ learning behaviors, learning results are often characterized by learners’ performance.With China’s distance education scale expands rapidly,the teaching platform of distance education institutions have accumulated a lot of distance learners’ learning data,including learning behavior data and academic performance data. In the background of "big data" and "cloud computing" era, to fully utilize and analyze big data of distance education will help us to know about the present situation of the modern distance education, grasp the learning rules of distance learners, to develop more suitable learning resources for distance learners, to provide better learning support services, and ultimately improve the quality of modern distance education in China.In this study part of the distance learners as the research object from Distance and Continuing Education College of Beijing Jiaotong University, mainly for the study of the relationship between learners’ participation and performance. Firstly, through data mining and correcting to extract the data of learners’behavior and performance from the teaching platform.secondly, sorting the learning behavior data and calculates the participation of each learning behavior; finally,analysis of each item the relationship between the learning participation and performance, drawn relevant conclusion and give corresponding suggestions.In this study, the author through viewing and analysing the existing teaching platform and relevant training manual, extracted the extraction ratio calculated eight learning participation data respectively reading course announcement participation, viewing course guidance participation, asking course questions participation, posting Participation, Submitting Offline Homework Participation, Finishing Test Participation, viewing courseware participation, login platform participation, and exported the consolidated results of learners (usual performance and test scores combined) from the platform. Finally, using SPSS to analyze the correlation between participation in the learning and performance as well as partly participation, draw10relevant conclusions and put forward some suggestions.