多源数据融合视角下的大学生“消费-学业-社交”画像构建研究

[目的/意义]挖掘高校学生数据构建学生画像,使高校管理过程中的学生形象具体化,利用数据分析手段深入了解学生需求,着力提升高校信息管理水平,推进管理和服务智能化。[方法/过程]基于高校管理和服务过程产生的多源数据,聚焦消费、学业和社交3类指标,利用MySQL和SPSS手段构建学生个体画像,利用Python中sklearn工具实现K-means聚类算法,构建学生群体画像,开展学生画像实证研究,并剖析学生画像的应用表征。[结果/结论]多源数据融合视角下的学生画像可以从个体和群体两个维度构建,个体画像表现直观,群体画像区分显著。可实现异常识别与预警、群体关注与引导和资源规划与调节等方面应用,有利于增加高校管理精度,提升学生获得感,为高校贫困助学、学业帮扶和心理干预等工作提供参考。

Construction of College Students' "Consumption-Academic-Social" Profiles from the Perspective of Multi-source Data Fusion

[Purpose/Significance] Mining college student data and constructing studnet profiles is conducive to in-depth understanding of students' needs, improving management level, and promoting intelligent service. [Method/Process] Based on the multi-source data mainly generated by the management and service process of colleges and universities, student profiles were developed by focusing on consumption, academic and social indicators, analyzing the characteristics of students, using the Scikit-Learn tool of Python, and applying the K-means clustering algorithms. Empirical research was carried out and representativeness of student portraits from individual and group perspectives was studied. [Results/Conclusions] First, this paper attempts to utilize a new data fusion perspective, by fusing explicit data with implicit data, and generating three-dimensional indicators of consumption behavior, academic behavior, and social behavior. Secondly, in order to solve the problem of single application scenario in previous research, the method of user profile construction is used to realize the fusion of multiple scenarios. Finally, based on the real student data, this study uses K-means clustering algorithm to select groups of students with different characteristics on the basis of previous research. The data of college students is analyzed, and further empirical research is carried out to describe the "consumption-academic-social" profiles of college students. Constructing student profiles from the perspective of multi-source data fusion can effectively provide a basis for decision-making by different units in colleges and universities, such as academic affairs,. Especially in the post-epidemic era, the profiles of college students can detect potential risks in time. The study found that at the individual level, by interpreting the label information of students' portraits, it is possible to understand the 3 aspects of students' consumption, academics and social interaction, and realize dynamic monitoring of individual students. At the group level, through cluster analysis, students with different characteristics can be selected, especially in terms of consumption behavior, and the characteristics of students' activity and stability can be deeply analyzed, which can not only provide a basis for the macro-level observation of students, but also provide new ideas for exploring the correlation between different behavioral elements of students. At the application level, the integration of multi-scenario student profiles can simultaneously realize abnormal identification and early warning, group attention and guidance, and resource planning and adjustment, which greatly broadens the application scenarios of research and improves the energy efficiency of education and teaching management in colleges and universities. However, due to the limitations of data and algorithms, the accuracy and ease of use of student portraits still need to be improved. There are both constraints from practical conditions and insufficient research methods. In future research, more extensive research should be used to improve college student profile construction system, and constantly develop more suitable techniques.