Integrated unsupervised student behavior clustering method

Abstract

The invention provides an integrated unsupervised student behavior clustering method aiming at the limitation of a questionnaire method in the aspect of data collection and the serious dependence of astatistical method, a supervised learning method and a semi-supervised learning method on student tags. The method comprises the following steps: firstly, extracting characteristics of student behavior data, dividing the characteristics into three parts, describing a centralized trend of the data by utilizing a mode, an average value and a range, expressing a discrete situation of the data by utilizing a minimum value, a first quantile, a median, a third quantile and a maximum value, and measuring a law degree of behavior occurrence time and a law degree of a behavior place by utilizing Shannon entropy; then, selecting the optimal behavior characteristics through variance and correlation analysis; and finally, carrying out initial clustering on the behavior characteristics of the studentsby utilizing DBSCAN, and further subdividing the super-large cluster by adopting K-means to obtain a final clustering result. The method does not depend on student tags, clustering is completed onlyby analyzing behavior data, and a foundation is laid for refined service and management of students.

Description

technical field [0001] The present invention relates to an integrated unsupervised student behavior clustering method, in particular to an unsupervised student behavior clustering method that integrates a density-based clustering algorithm DBSCAN and a distance-based division clustering algorithm K-means . Background technique [0002] Understanding students' behavior patterns in a timely and accurate manner and taking targeted measures play an important role in optimizing the education and teaching process and improving the quality of education. For example, by analyzing students' learning behaviors on online teaching platforms or traditional classrooms, teachers can be reminded to adjust teaching plans and methods to achieve better teaching results; by analyzing students' movement patterns on campus, administrators can manage resources By analyzing students' social behavior patterns, we can find lonely students and give them timely attention, and carry out psychological i...

AI Technical Summary

Problems solved by technology

However, this method has the following limitations: (1) Questionnaires are usually distributed regularly, such as once every semester or every school year. This non-real-time survey method cannot meet the requirements of real-time applications. Abnormal behavior patterns and take necessary interventions to avoid accidents

(2) Students with abnormal behavior may deliberately fill in false information to make them behave normally, while students who are truly normal may cause their analysis results to be abnormal because they fill in the questionnaire at will. These noise samples will cause certain errors in the analysis results

(3) Designing a questionnaire that can accurately and comprehensively understand students' behavior patterns requires extensive domain knowledge, which brings great challenges to designers

In practical applications, due to privacy protection and other reasons, it is usually impossible to obtain student label information, or the acquisition cost is high, so unsupervised clustering methods are widely used

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