Dynamic early warning system of college students’ target course performance based on improved Apriori algorithm

The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

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Research on Algorithm in Association Rule in an Early Warning System of College Students

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.445 ◽

2011 ◽

Vol 460-461 ◽

pp. 445-450

Author(s):

Li Juan Zhou ◽

Shuang Li ◽

Zhang Zhang

Keyword(s):

College Students ◽

Early Warning ◽

Association Rule ◽

Early Warning System ◽

High Efficiency ◽

Warning System ◽

Frequent Itemset ◽

Apriori Algorithm ◽

Very High

The student early warning is a type of the problem that involves intelligence factor and non-intelligence factor and indetermination factor, the challenge lies in ascertaining early-warning factor. In this paper, basing on analyzing association rule and the principle of Apriori algorithm, a new Apriori algorithm has been designed for solving complexity of predictors in an early warning system of college students. At first, make sure the count K of item gathering the largest frequent itemset. Then directly make the largest frequent itemset LK by the items, whose count of item is larger than K or equal to K. We can acquire the k-1、k-2 frequent itemset with the same method. We carry on an experiment on the new algorithm. As a result, the efficiency of the new Algorithm is raised obviously. And along with data quantity aggrandizement, the time consumed changes inconspicuously. That is to say the algorithm keeps very high efficiency.

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RESEARCH ON EARLY WARNING SYSTEM OF COLLEGE STUDENTS’ BEHAVIOR BASED ON BIG DATA ENVIRONMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-659-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 659-665

Author(s):

C. Y. Yang ◽

J. Y. Liu ◽

S. Huang

Keyword(s):

College Students ◽

Big Data ◽

Early Warning ◽

Behavioral Problems ◽

Early Warning System ◽

Warning System ◽

Safety Control ◽

Education Management ◽

Data Environment ◽

Behavior Based

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.

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