Design and Implementation of Early Warning System Based on Educational Big Data

With the continuous popularization of higher education, the academic problems of university students are constantly emerging. Due to the lack of a systematic learning guidance system, students are lack of learning ability, poor binding force and strong dependence. Because of disciplinary violations and academic problems, quite a few students have been delayed in graduation, processed or even dropped out of school. In order to improve this situation as soon as possible, many colleges and universities have established academic warning system one after another. In the previous systems, it is basically based on performance score, credit score and other performance data, and then different warning levels are manually recorded. Without comprehensive relevant data, the single inefficient forms can not guarantee the effectiveness of academic monitoring and early warning. Dependent on the data of teaching and library, this paper suggests an academic early warning system in Hangzhou Normal University. Considering the data of educational administration, library borrowing and self-study, an early-warning model of learning is established after comprehensive analysis. By this model, we can discover and identify the existing and potential academic problems of students in the early stage of college, and inform themselves and their parents to urge students to correct their attitude and study more efficiently.