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

机译：基于教育大数据的预警系统设计与实现

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摘要

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.
机译：随着高等教育的不断普及，大学生的学术问题不断出现。由于缺乏系统的学习指导体系，学生缺乏学习能力，约束力差，依赖性强。由于违反纪律和学术问题，很多学生的毕业延误，处理甚至辍学。为了尽快改善这种状况，许多高校纷纷建立了学术预警系统。在以前的系统中，它基本上是基于性能分数，信用分数和其他性能数据，然后手动记录不同的警告级别。没有全面的相关数据，单一的低效形式将无法保证学术监测和预警的有效性。根据教学和图书馆的数据，本文提出了杭州师范大学的学术预警系统。综合教育管理，图书馆借阅和自学等方面的数据，经过综合分析，建立了预警学习模型。通过这种模型，我们可以发现并识别出大学早期学生存在的和潜在的学术问题，并告知自己和父母，以敦促学生纠正他们的态度并更有效地学习。

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